New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI agents run.
For the last two years, the enterprise AI race has mostly been framed as a model war: OpenAI’s GPT series versus Anthropic’s Claude versus Google’s Gemini, with smaller and open-source alternatives also coming in from the U.S. and China.
But the next strategic fight may not be over which model answers a prompt best. It may be over who controls the layer where agents plan, call tools, access data, run workflows and prove to security teams that they did not do anything they were not supposed to do.
New VB Pulse survey data suggests the category is already taking shape. Our independent Enterprise Agentic Orchestration tracker, a survey that records the preferences of qualified, verified technical-decision maker respondents at enterprises at regular intervals, found that Microsoft Copilot Studio and Azure AI Studio led with 38.6% primary-platform adoption in February, up from 35.7% in January.
OpenAI’s Assistants and Responses API held second place, rising from 23.2% to 25.7%.
Anthropic remained far smaller, but it made its first appearance in the tracker: moving from 0% in January to 5.7% in February for Anthropic tool use and workflows.
The underlying move is small — four respondents out of a total 70 in this cohort, with more to come — but strategically interesting because it marks the first sign in this tracker of Claude usage moving from the model layer into native orchestration.
That distinction matters. Enterprises are not merely choosing chatbots. They are deciding where the live operational machinery of AI work will sit: inside Microsoft’s stack, inside OpenAI’s API layer, inside Anthropic’s managed runtime, inside an open framework, or across a hybrid mix of all of them.
“This is the convergence moment for enterprise AI,” said Tom Findling, CEO and cofounder of AI cybsersecurity startup Conifers, in a statement to VentureBeat. “Models and agent frameworks have matured enough together that enterprises are now shifting focus beyond model quality to the control plane around it. In security operations, we’re seeing the competitive advantage move toward platforms that can orchestrate agents, leverage enterprise context, and provide governance and auditability across customer environments.”
Anthropic’s number is still small to start — but the increase is not
The Anthropic number, by itself, should not be overread. A move from zero to 5.7% is not a juggernaut. It is not proof that Anthropic has captured enterprise orchestration.
It is not even enough to say Anthropic has a durable lead in any part of this market. Microsoft owns the early enterprise distribution advantage, and OpenAI has a much larger installed base in orchestration than Anthropic.
But small numbers can matter when they appear at the start of a new market structure. Anthropic’s emergence in orchestration comes as the broader VB Pulse data shows Claude also gaining massive enterprise adoption at the model layer.
In our VB Pulse Q1 Foundation Models and Intelligence Platforms tracker, Anthropic rose from 23.9% in January to 28.6% in February and then even more dramatically to 56.2% in March among qualified enterprise respondents, with the March reading flagged as directional only, because the sample was only 16 respondents.
The story, then, is not that Anthropic is winning orchestration today. It is that Anthropic’s model momentum may be starting to spill into the orchestration layer.
That is where the strategic stakes get higher.
A model is easier to swap than an agent runtime
A model is relatively easy to swap, at least in theory. A company can route one workload to Claude, another to GPT, another to Gemini and another to a smaller open model.
In fact, the VB Pulse Foundation Models tracker over the same Q1 period shows that multi-model strategy is the enterprise consensus: respondents increasingly report adopting multiple models and building orchestration layers that route across them by task, cost and risk profile.
An agent runtime is different. Once a company’s workflows, tool permissions, credentials, audit logs, memory, sandboxed execution and operational monitoring live inside one provider’s environment, switching providers becomes less like changing models and more like changing infrastructure.
That is the real reason Anthropic’s 5.7% foothold is worth watching
Anthropic has already made clear that it wants to provide more than the model. Its Claude Managed Agents documentation describes a public beta for a managed agent harness with secure sandboxing, built-in tools and API-run sessions, while Anthropic’s engineering post frames the architecture around decoupling the model from the surrounding agent machinery: the session, the harness and the sandbox.
In plain English, Anthropic is trying to host the environment where Claude agents remember context, use tools, run code, operate inside sandboxes and persist across long-running workflows. That is no longer just inference. That is operational infrastructure.
The pitch is obvious: most enterprises do not want to stitch together their own agent stack from scratch. They want agents that can act, but they also want permission boundaries, audit trails, workflow reliability and ways to stop the system when something goes wrong.
Security is becoming the buying criterion
The VB Pulse orchestration tracker shows that buyers are prioritizing exactly those concerns. Security and permissions ranked as the top orchestration platform selection criterion in both January and February, at 39.3% and 37.1%.
Control over agent execution rose from 17.9% to 22.9%, while flexibility across models and tools fell from 35.7% to 25.7%. The market appears to be shifting from optionality toward governance.
That shift is not surprising. A chatbot can be wrong and still remain mostly contained. An agent that can send emails, modify documents, query databases, call APIs or execute workflows has a much larger blast radius. The enterprise question is not only whether the agent is smart enough.
It is who gave it permission, what it touched, what it changed, whether those actions were logged, and whether the company can unwind the damage if something goes wrong.
Ev Kontsevoy, cofounder and CEO of Teleport, an identity and digital infrastructure solutions company, argues that the industry is still putting too much emphasis on orchestration itself and not enough on identity: “The race to own the agent orchestration layer is real,” Kontsevoy said. “It’s also solving the wrong problem first. Orchestration without identity only multiplies chaos. Without identity, you don’t know what an agent can access, what it actually did, or how to revoke its access when it operates outside policy. A unified identity layer is a prerequisite to deploying agents — one or many — in infrastructure.”
Syam Nair, Chief Product Officer at the enterprise unified data storage solutions firm NetApp, believes data management is key in all cases to secure AI agent orchestration across the enterprise. As he said in a statement to VentureBeat: «Effective agent management requires built-in intelligence and a continuously updated understanding of both data and, critically, its metadata. This visibility allows organizations to define and enforce clear policies so data is used only by the right agents, for the right purposes. Making this work at scale is a crossfunctional effort. Security, storage, and data science teams must work together to implement policies that safeguard company data, while creating a strong data foundation for AI.»
He continued: «The CIOs and technology leaders that are successful are the ones who take the input, policies, and vision from all these teams into account as they build a data infrastructure that minimizes risk and drives business value.»
Microsoft has the distribution edge
That is why Microsoft’s early lead makes sense. Copilot Studio and Azure AI Studio sit inside an enterprise stack many companies already use: Microsoft 365, Teams, Entra ID, Azure and existing procurement relationships.
The VB Pulse Orchestration Tracker for Q1 2026 describes Microsoft as the enterprise default, with no other platform within 13 percentage points in February.
David Weston, CVP, AI Security, Microsoft, provided some insight on why, writing in a statement to VentureBeat: «Without a unified control layer, you start to see fragmentation – agents operating in silos, inconsistent governance, and gaps in security. What customers are asking for is a way to bring order to that complexity. With Agent 365, we’re providing a single control plane to observe, govern, and secure agents across Microsoft, partner, and third-party ecosystems, all grounded in enterprise data and identity.»
OpenAI’s second-place position is also unsurprising. Its Assistants and Responses API gave developers an early way to build agent-like systems using OpenAI’s models and tooling. In the orchestration tracker, OpenAI is not surging, but it is still ticking up steadily: 23.2% in January to 25.7% in February.
Anthropic is the newcomer at the orchestration layer. But its timing may be favorable. The VB Pulse Foundation Models tracker for Q1 2026 suggests enterprises increasingly see Claude as a fit for higher-stakes workloads where safety, instruction following, long context and governance matter.
The orchestration tracker suggests those same buyers are now moving from agent experiments toward production workflows, where security, permissions and task reliability become the gating issues.
That creates a possible path for Anthropic: not to beat Microsoft as the default enterprise platform, at least not immediately, but to become the agent runtime for companies that already trust Claude for sensitive or complex workloads.
The risk is lock-in
The risk for enterprises is lock-in.
The orchestration tracker found that a hybrid control plane — combining provider-native orchestration with external orchestration — was the leading expected architecture, holding around 35% to 36% across the two substantive waves.
Provider-managed-only approaches grew modestly but remained a minority. The report’s conclusion is blunt: enterprises are not willing to give full orchestration control to any single provider.
It makes total sense as enterprises seek to leverage the «best-in-breed» models, harnesses, and tools from multiple vendors, especially as their needs differ widely across sector, business, and size.
«Most enterprises will operate in a multi-model, multi-agent environment, which makes an independent control plane essential,» agreed Felix Van de Maele, CEO of Collibra, a unified data governance startup for AI, in a statement to VentureBeat. «That is why we built AI Command Center: to give organizations the visibility, governance, and real-time oversight needed to manage AI systems and agents across the full lifecycle.»
That caution shows up in the risk data. When asked about risks if agent control lives inside a model provider platform, respondents cited security and permissioning limitations as the top concern. Vendor lock-in was the second-largest concern and the only one that increased from January to February, rising from 23.2% to 25.7%.
This is the tension at the heart of the agent market. Enterprises want managed infrastructure because building reliable agents is hard. But the more a provider manages, the more it may own.
Dr. Rania Khalaf, chief AI officer at WSO2 — the subsidiary of EQT that offers open source, customizable AI stacks for enterprises — said enterprises will need an agent control plane that sits apart from individual frameworks, harnesses and runtimes because agents combine the unpredictability of LLMs with the ability to take actions that have consequences.
“Teams want the freedom to use the best model and framework for each job — Claude for coding, Gemini for writing, LangGraph or CrewAI for dynamic modular behavior — and that heterogeneity makes consistent governance untenable in integrated platforms that lock into one ecosystem,” Khalaf said.
From LLMOps to Agent Ops
Khalaf said the industry is also moving from MLOps to LLMOps to “Agent Ops,” where governance has to cover the whole agent, not just the model call.
“A guardrail on an LLM call can catch hallucination or toxic output, but it will not catch an agent thrashing in an unbreakable, costly loop, which is why governance now has to extend out from the LLM interaction to the scope of the agent,” she said.
The practical implication is that enterprises need to separate policy and control from the agent logic itself. Khalaf pointed to the recent example of an agent deleting a production database despite being told not to, arguing that the failure showed the limits of relying on prompt-level instructions where hard identity and access controls are needed.
“Pulling guardrails, evals, policies, bindings, and agent identity out of the core agent logic allows them to be configured per deployment and per environment, owned by the appropriate teams in security, product, and compliance, without fragmenting the governance layer as different teams choose different models and frameworks,” Khalaf said.
MCP is open. The runtime may still be sticky
That is where Anthropic’s Model Context Protocol, or MCP, complicates the story. MCP is not a walled garden; Anthropic introduced it as an open standard for connecting AI systems to data and tools, and Anthropic’s documentation describes MCP as an open-source standard for connecting AI applications to external systems.
But openness at the protocol layer does not automatically eliminate lock-in at the runtime layer. An enterprise could use an open protocol to connect tools while still becoming dependent on a provider’s managed sessions, logs, sandboxes, permissions model, workflow state and deployment environment. In other words, MCP may reduce integration friction, while managed agent infrastructure could still increase switching costs.
Khalaf said Microsoft’s lead likely reflects its M365 and Azure distribution, while Anthropic’s emerging foothold could reflect a different architectural bet around open protocols such as MCP. But she argued the long-term direction is not a single-provider stack.
“Enterprises serious about running agents in production will end up multi-vendor across these layers,” Khalaf said, “which is why the open and interoperable control plane matters more than the current percentages might suggest.”
The next cycle may be cross-vendor collaboration
That same tension — between provider-native convenience and cross-vendor reality — is where Arick Goomanovsky, CEO and cofounder of universal AI agent orchestrator startup BAND, sees the next competitive cycle forming.
“Enterprises now run agents everywhere: individual assistants and coding agents, multi-agent systems in production, agents embedded in Agentforce and ServiceNow, and third-party agents consumed as agent-as-a-service,” Goomanovsky said. “None of them collaborate across those boundaries by default.”
Goomanovsky argues that the missing layer is not just orchestration inside a single model provider, but a cross-vendor collaboration layer that lets agents from different ecosystems act together.
“What’s emerging in parallel is demand for an agentic collaboration harness – an interaction layer that lets agents from Microsoft, OpenAI, Anthropic, and internal teams operate as one workforce,” he said. “Orchestration inside any single vendor is still a walled garden so the next competitive cycle is cross-vendor agent collaboration.”
Independent frameworks face an enterprise packaging problem
There is also a warning sign for independent orchestration frameworks. LangChain and LangGraph fell from 5.4% to 1.4% as the primary orchestration platform in the qualified enterprise sample.
External orchestration abstracted entirely from model providers also fell from 8.9% to 2.9%.
Scott Likens, Global Chief AI Engineer at professional services giant PwC, has a front row seat to this trend as the company spearheads and assists clients with their AI transformations.
As he told VentureBeat in a statement: «Right now, most enterprises are still operating in fragmented environments, with orchestration spread across platforms, business applications, and internally developed tooling. Over time, the market will likely move toward more unified orchestration models, but interoperability, governance and security will remain critical because enterprises are unlikely to standardize on a single agent ecosystem.»
The report argues that fully independent orchestration frameworks may not yet have the enterprise packaging — security certifications, support, compliance documentation and vendor accountability — that procurement teams require.
That does not mean open frameworks are irrelevant. It does suggest that enterprise buyers may increasingly consume open or developer-first orchestration through managed products, cloud-provider partnerships or internal control planes rather than as standalone frameworks.
The agent market starts to look like cloud infrastructure
This is where the agent market starts to look less like the early chatbot market and more like enterprise cloud infrastructure. The winning vendors will not only have capable models. They will have identity integration, permission controls, audit logs, observability, workflow tooling, sandboxing, evaluation and a credible answer to who owns the control plane.
Indeed, the orchestration layer is but one part of the stack that the enterprise must fill in, and enterprises may actually decide to have different orchestration layers for agents working in different departments and functions.
As Nithya Lakshmanan, Chief Product Officer at revenue team AI orchestration startup Outreach.ai wrote in a statement to VentureBeat: «General-purpose orchestration platforms coordinate agent activity well, but they don’t carry the workflow-specific context that determines whether an agent’s action is correct for a given situation. In revenue workflows, an agent acting on incomplete deal history or missing buyer context will underperform and erode trust with users. The teams getting the most out of multi-agent systems are treating domain-specific data as the governance layer, with orchestration sitting on top. Most enterprises have chosen their orchestration stack, and what they’re now figuring out is how those platforms get access to the workflow context they need to make agents useful inside specific business functions.»
That is why Anthropic — which is increasingly launching its own domain-specific agents for finance and design, among other categories — is worth following closely. The company does not need to win the entire orchestration market tomorrow for its strategy to matter. It only needs to persuade a growing set of Claude enterprise customers to let Anthropic handle more of the surrounding machinery: tools, workflows, memory, execution and governance.
If it succeeds, Claude becomes more than a model in a multi-model portfolio. It becomes part of the infrastructure where enterprise work gets done.
That would put Anthropic in a more direct fight with OpenAI and Microsoft — not just over model quality, but over the operating layer of AI agents.
The narrow but important read
The safe interpretation of the VB Pulse data is narrow but important: Anthropic is not yet a major enterprise orchestration platform. Microsoft is. OpenAI is much closer. But Anthropic has registered its first measurable foothold at the orchestration layer, just as the market is deciding who should control agent execution.
For enterprise buyers, that may be the question that matters most in 2026. Not which model is best, but which provider gets to run the agent — and how hard it will be to leave once the agent is running.
New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI agents run.
For the last two years, the enterprise AI race has mostly been framed as a model war: OpenAI’s GPT series versus Anthropic’s Claude versus Google’s Gemini, with smaller and open-source alternatives also coming in from the U.S. and China.
But the next strategic fight may not be over which model answers a prompt best. It may be over who controls the layer where agents plan, call tools, access data, run workflows and prove to security teams that they did not do anything they were not supposed to do.
New VB Pulse survey data suggests the category is already taking shape. Our independent Enterprise Agentic Orchestration tracker, a survey that records the preferences of qualified, verified technical-decision maker respondents at enterprises at regular intervals, found that Microsoft Copilot Studio and Azure AI Studio led with 38.6% primary-platform adoption in February, up from 35.7% in January.
OpenAI’s Assistants and Responses API held second place, rising from 23.2% to 25.7%.
Anthropic remained far smaller, but it made its first appearance in the tracker: moving from 0% in January to 5.7% in February for Anthropic tool use and workflows.
The underlying move is small — four respondents out of a total 70 in this cohort, with more to come — but strategically interesting because it marks the first sign in this tracker of Claude usage moving from the model layer into native orchestration.
That distinction matters. Enterprises are not merely choosing chatbots. They are deciding where the live operational machinery of AI work will sit: inside Microsoft’s stack, inside OpenAI’s API layer, inside Anthropic’s managed runtime, inside an open framework, or across a hybrid mix of all of them.
“This is the convergence moment for enterprise AI,” said Tom Findling, CEO and cofounder of AI cybsersecurity startup Conifers, in a statement to VentureBeat. “Models and agent frameworks have matured enough together that enterprises are now shifting focus beyond model quality to the control plane around it. In security operations, we’re seeing the competitive advantage move toward platforms that can orchestrate agents, leverage enterprise context, and provide governance and auditability across customer environments.”
Anthropic’s number is still small to start — but the increase is not
The Anthropic number, by itself, should not be overread. A move from zero to 5.7% is not a juggernaut. It is not proof that Anthropic has captured enterprise orchestration.
It is not even enough to say Anthropic has a durable lead in any part of this market. Microsoft owns the early enterprise distribution advantage, and OpenAI has a much larger installed base in orchestration than Anthropic.
But small numbers can matter when they appear at the start of a new market structure. Anthropic’s emergence in orchestration comes as the broader VB Pulse data shows Claude also gaining massive enterprise adoption at the model layer.
In our VB Pulse Q1 Foundation Models and Intelligence Platforms tracker, Anthropic rose from 23.9% in January to 28.6% in February and then even more dramatically to 56.2% in March among qualified enterprise respondents, with the March reading flagged as directional only, because the sample was only 16 respondents.
The story, then, is not that Anthropic is winning orchestration today. It is that Anthropic’s model momentum may be starting to spill into the orchestration layer.
That is where the strategic stakes get higher.
A model is easier to swap than an agent runtime
A model is relatively easy to swap, at least in theory. A company can route one workload to Claude, another to GPT, another to Gemini and another to a smaller open model.
In fact, the VB Pulse Foundation Models tracker over the same Q1 period shows that multi-model strategy is the enterprise consensus: respondents increasingly report adopting multiple models and building orchestration layers that route across them by task, cost and risk profile.
An agent runtime is different. Once a company’s workflows, tool permissions, credentials, audit logs, memory, sandboxed execution and operational monitoring live inside one provider’s environment, switching providers becomes less like changing models and more like changing infrastructure.
That is the real reason Anthropic’s 5.7% foothold is worth watching
Anthropic has already made clear that it wants to provide more than the model. Its Claude Managed Agents documentation describes a public beta for a managed agent harness with secure sandboxing, built-in tools and API-run sessions, while Anthropic’s engineering post frames the architecture around decoupling the model from the surrounding agent machinery: the session, the harness and the sandbox.
In plain English, Anthropic is trying to host the environment where Claude agents remember context, use tools, run code, operate inside sandboxes and persist across long-running workflows. That is no longer just inference. That is operational infrastructure.
The pitch is obvious: most enterprises do not want to stitch together their own agent stack from scratch. They want agents that can act, but they also want permission boundaries, audit trails, workflow reliability and ways to stop the system when something goes wrong.
Security is becoming the buying criterion
The VB Pulse orchestration tracker shows that buyers are prioritizing exactly those concerns. Security and permissions ranked as the top orchestration platform selection criterion in both January and February, at 39.3% and 37.1%.
Control over agent execution rose from 17.9% to 22.9%, while flexibility across models and tools fell from 35.7% to 25.7%. The market appears to be shifting from optionality toward governance.
That shift is not surprising. A chatbot can be wrong and still remain mostly contained. An agent that can send emails, modify documents, query databases, call APIs or execute workflows has a much larger blast radius. The enterprise question is not only whether the agent is smart enough.
It is who gave it permission, what it touched, what it changed, whether those actions were logged, and whether the company can unwind the damage if something goes wrong.
Ev Kontsevoy, cofounder and CEO of Teleport, an identity and digital infrastructure solutions company, argues that the industry is still putting too much emphasis on orchestration itself and not enough on identity: “The race to own the agent orchestration layer is real,” Kontsevoy said. “It’s also solving the wrong problem first. Orchestration without identity only multiplies chaos. Without identity, you don’t know what an agent can access, what it actually did, or how to revoke its access when it operates outside policy. A unified identity layer is a prerequisite to deploying agents — one or many — in infrastructure.”
Syam Nair, Chief Product Officer at the enterprise unified data storage solutions firm NetApp, believes data management is key in all cases to secure AI agent orchestration across the enterprise. As he said in a statement to VentureBeat: «Effective agent management requires built-in intelligence and a continuously updated understanding of both data and, critically, its metadata. This visibility allows organizations to define and enforce clear policies so data is used only by the right agents, for the right purposes. Making this work at scale is a crossfunctional effort. Security, storage, and data science teams must work together to implement policies that safeguard company data, while creating a strong data foundation for AI.»
He continued: «The CIOs and technology leaders that are successful are the ones who take the input, policies, and vision from all these teams into account as they build a data infrastructure that minimizes risk and drives business value.»
Microsoft has the distribution edge
That is why Microsoft’s early lead makes sense. Copilot Studio and Azure AI Studio sit inside an enterprise stack many companies already use: Microsoft 365, Teams, Entra ID, Azure and existing procurement relationships.
The VB Pulse Orchestration Tracker for Q1 2026 describes Microsoft as the enterprise default, with no other platform within 13 percentage points in February.
David Weston, CVP, AI Security, Microsoft, provided some insight on why, writing in a statement to VentureBeat: «Without a unified control layer, you start to see fragmentation – agents operating in silos, inconsistent governance, and gaps in security. What customers are asking for is a way to bring order to that complexity. With Agent 365, we’re providing a single control plane to observe, govern, and secure agents across Microsoft, partner, and third-party ecosystems, all grounded in enterprise data and identity.»
OpenAI’s second-place position is also unsurprising. Its Assistants and Responses API gave developers an early way to build agent-like systems using OpenAI’s models and tooling. In the orchestration tracker, OpenAI is not surging, but it is still ticking up steadily: 23.2% in January to 25.7% in February.
Anthropic is the newcomer at the orchestration layer. But its timing may be favorable. The VB Pulse Foundation Models tracker for Q1 2026 suggests enterprises increasingly see Claude as a fit for higher-stakes workloads where safety, instruction following, long context and governance matter.
The orchestration tracker suggests those same buyers are now moving from agent experiments toward production workflows, where security, permissions and task reliability become the gating issues.
That creates a possible path for Anthropic: not to beat Microsoft as the default enterprise platform, at least not immediately, but to become the agent runtime for companies that already trust Claude for sensitive or complex workloads.
The risk is lock-in
The risk for enterprises is lock-in.
The orchestration tracker found that a hybrid control plane — combining provider-native orchestration with external orchestration — was the leading expected architecture, holding around 35% to 36% across the two substantive waves.
Provider-managed-only approaches grew modestly but remained a minority. The report’s conclusion is blunt: enterprises are not willing to give full orchestration control to any single provider.
It makes total sense as enterprises seek to leverage the «best-in-breed» models, harnesses, and tools from multiple vendors, especially as their needs differ widely across sector, business, and size.
«Most enterprises will operate in a multi-model, multi-agent environment, which makes an independent control plane essential,» agreed Felix Van de Maele, CEO of Collibra, a unified data governance startup for AI, in a statement to VentureBeat. «That is why we built AI Command Center: to give organizations the visibility, governance, and real-time oversight needed to manage AI systems and agents across the full lifecycle.»
That caution shows up in the risk data. When asked about risks if agent control lives inside a model provider platform, respondents cited security and permissioning limitations as the top concern. Vendor lock-in was the second-largest concern and the only one that increased from January to February, rising from 23.2% to 25.7%.
This is the tension at the heart of the agent market. Enterprises want managed infrastructure because building reliable agents is hard. But the more a provider manages, the more it may own.
Dr. Rania Khalaf, chief AI officer at WSO2 — the subsidiary of EQT that offers open source, customizable AI stacks for enterprises — said enterprises will need an agent control plane that sits apart from individual frameworks, harnesses and runtimes because agents combine the unpredictability of LLMs with the ability to take actions that have consequences.
“Teams want the freedom to use the best model and framework for each job — Claude for coding, Gemini for writing, LangGraph or CrewAI for dynamic modular behavior — and that heterogeneity makes consistent governance untenable in integrated platforms that lock into one ecosystem,” Khalaf said.
From LLMOps to Agent Ops
Khalaf said the industry is also moving from MLOps to LLMOps to “Agent Ops,” where governance has to cover the whole agent, not just the model call.
“A guardrail on an LLM call can catch hallucination or toxic output, but it will not catch an agent thrashing in an unbreakable, costly loop, which is why governance now has to extend out from the LLM interaction to the scope of the agent,” she said.
The practical implication is that enterprises need to separate policy and control from the agent logic itself. Khalaf pointed to the recent example of an agent deleting a production database despite being told not to, arguing that the failure showed the limits of relying on prompt-level instructions where hard identity and access controls are needed.
“Pulling guardrails, evals, policies, bindings, and agent identity out of the core agent logic allows them to be configured per deployment and per environment, owned by the appropriate teams in security, product, and compliance, without fragmenting the governance layer as different teams choose different models and frameworks,” Khalaf said.
MCP is open. The runtime may still be sticky
That is where Anthropic’s Model Context Protocol, or MCP, complicates the story. MCP is not a walled garden; Anthropic introduced it as an open standard for connecting AI systems to data and tools, and Anthropic’s documentation describes MCP as an open-source standard for connecting AI applications to external systems.
But openness at the protocol layer does not automatically eliminate lock-in at the runtime layer. An enterprise could use an open protocol to connect tools while still becoming dependent on a provider’s managed sessions, logs, sandboxes, permissions model, workflow state and deployment environment. In other words, MCP may reduce integration friction, while managed agent infrastructure could still increase switching costs.
Khalaf said Microsoft’s lead likely reflects its M365 and Azure distribution, while Anthropic’s emerging foothold could reflect a different architectural bet around open protocols such as MCP. But she argued the long-term direction is not a single-provider stack.
“Enterprises serious about running agents in production will end up multi-vendor across these layers,” Khalaf said, “which is why the open and interoperable control plane matters more than the current percentages might suggest.”
The next cycle may be cross-vendor collaboration
That same tension — between provider-native convenience and cross-vendor reality — is where Arick Goomanovsky, CEO and cofounder of universal AI agent orchestrator startup BAND, sees the next competitive cycle forming.
“Enterprises now run agents everywhere: individual assistants and coding agents, multi-agent systems in production, agents embedded in Agentforce and ServiceNow, and third-party agents consumed as agent-as-a-service,” Goomanovsky said. “None of them collaborate across those boundaries by default.”
Goomanovsky argues that the missing layer is not just orchestration inside a single model provider, but a cross-vendor collaboration layer that lets agents from different ecosystems act together.
“What’s emerging in parallel is demand for an agentic collaboration harness – an interaction layer that lets agents from Microsoft, OpenAI, Anthropic, and internal teams operate as one workforce,” he said. “Orchestration inside any single vendor is still a walled garden so the next competitive cycle is cross-vendor agent collaboration.”
Independent frameworks face an enterprise packaging problem
There is also a warning sign for independent orchestration frameworks. LangChain and LangGraph fell from 5.4% to 1.4% as the primary orchestration platform in the qualified enterprise sample.
External orchestration abstracted entirely from model providers also fell from 8.9% to 2.9%.
Scott Likens, Global Chief AI Engineer at professional services giant PwC, has a front row seat to this trend as the company spearheads and assists clients with their AI transformations.
As he told VentureBeat in a statement: «Right now, most enterprises are still operating in fragmented environments, with orchestration spread across platforms, business applications, and internally developed tooling. Over time, the market will likely move toward more unified orchestration models, but interoperability, governance and security will remain critical because enterprises are unlikely to standardize on a single agent ecosystem.»
The report argues that fully independent orchestration frameworks may not yet have the enterprise packaging — security certifications, support, compliance documentation and vendor accountability — that procurement teams require.
That does not mean open frameworks are irrelevant. It does suggest that enterprise buyers may increasingly consume open or developer-first orchestration through managed products, cloud-provider partnerships or internal control planes rather than as standalone frameworks.
The agent market starts to look like cloud infrastructure
This is where the agent market starts to look less like the early chatbot market and more like enterprise cloud infrastructure. The winning vendors will not only have capable models. They will have identity integration, permission controls, audit logs, observability, workflow tooling, sandboxing, evaluation and a credible answer to who owns the control plane.
Indeed, the orchestration layer is but one part of the stack that the enterprise must fill in, and enterprises may actually decide to have different orchestration layers for agents working in different departments and functions.
As Nithya Lakshmanan, Chief Product Officer at revenue team AI orchestration startup Outreach.ai wrote in a statement to VentureBeat: «General-purpose orchestration platforms coordinate agent activity well, but they don’t carry the workflow-specific context that determines whether an agent’s action is correct for a given situation. In revenue workflows, an agent acting on incomplete deal history or missing buyer context will underperform and erode trust with users. The teams getting the most out of multi-agent systems are treating domain-specific data as the governance layer, with orchestration sitting on top. Most enterprises have chosen their orchestration stack, and what they’re now figuring out is how those platforms get access to the workflow context they need to make agents useful inside specific business functions.»
That is why Anthropic — which is increasingly launching its own domain-specific agents for finance and design, among other categories — is worth following closely. The company does not need to win the entire orchestration market tomorrow for its strategy to matter. It only needs to persuade a growing set of Claude enterprise customers to let Anthropic handle more of the surrounding machinery: tools, workflows, memory, execution and governance.
If it succeeds, Claude becomes more than a model in a multi-model portfolio. It becomes part of the infrastructure where enterprise work gets done.
That would put Anthropic in a more direct fight with OpenAI and Microsoft — not just over model quality, but over the operating layer of AI agents.
The narrow but important read
The safe interpretation of the VB Pulse data is narrow but important: Anthropic is not yet a major enterprise orchestration platform. Microsoft is. OpenAI is much closer. But Anthropic has registered its first measurable foothold at the orchestration layer, just as the market is deciding who should control agent execution.
For enterprise buyers, that may be the question that matters most in 2026. Not which model is best, but which provider gets to run the agent — and how hard it will be to leave once the agent is running.
New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI agents run.
For the last two years, the enterprise AI race has mostly been framed as a model war: OpenAI’s GPT series versus Anthropic’s Claude versus Google’s Gemini, with smaller and open-source alternatives also coming in from the U.S. and China.
But the next strategic fight may not be over which model answers a prompt best. It may be over who controls the layer where agents plan, call tools, access data, run workflows and prove to security teams that they did not do anything they were not supposed to do.
New VB Pulse survey data suggests the category is already taking shape. Our independent Enterprise Agentic Orchestration tracker, a survey that records the preferences of qualified, verified technical-decision maker respondents at enterprises at regular intervals, found that Microsoft Copilot Studio and Azure AI Studio led with 38.6% primary-platform adoption in February, up from 35.7% in January.
OpenAI’s Assistants and Responses API held second place, rising from 23.2% to 25.7%.
Anthropic remained far smaller, but it made its first appearance in the tracker: moving from 0% in January to 5.7% in February for Anthropic tool use and workflows.
The underlying move is small — four respondents out of a total 70 in this cohort, with more to come — but strategically interesting because it marks the first sign in this tracker of Claude usage moving from the model layer into native orchestration.
That distinction matters. Enterprises are not merely choosing chatbots. They are deciding where the live operational machinery of AI work will sit: inside Microsoft’s stack, inside OpenAI’s API layer, inside Anthropic’s managed runtime, inside an open framework, or across a hybrid mix of all of them.
“This is the convergence moment for enterprise AI,” said Tom Findling, CEO and cofounder of AI cybsersecurity startup Conifers, in a statement to VentureBeat. “Models and agent frameworks have matured enough together that enterprises are now shifting focus beyond model quality to the control plane around it. In security operations, we’re seeing the competitive advantage move toward platforms that can orchestrate agents, leverage enterprise context, and provide governance and auditability across customer environments.”
Anthropic’s number is still small to start — but the increase is not
The Anthropic number, by itself, should not be overread. A move from zero to 5.7% is not a juggernaut. It is not proof that Anthropic has captured enterprise orchestration.
It is not even enough to say Anthropic has a durable lead in any part of this market. Microsoft owns the early enterprise distribution advantage, and OpenAI has a much larger installed base in orchestration than Anthropic.
But small numbers can matter when they appear at the start of a new market structure. Anthropic’s emergence in orchestration comes as the broader VB Pulse data shows Claude also gaining massive enterprise adoption at the model layer.
In our VB Pulse Q1 Foundation Models and Intelligence Platforms tracker, Anthropic rose from 23.9% in January to 28.6% in February and then even more dramatically to 56.2% in March among qualified enterprise respondents, with the March reading flagged as directional only, because the sample was only 16 respondents.
The story, then, is not that Anthropic is winning orchestration today. It is that Anthropic’s model momentum may be starting to spill into the orchestration layer.
That is where the strategic stakes get higher.
A model is easier to swap than an agent runtime
A model is relatively easy to swap, at least in theory. A company can route one workload to Claude, another to GPT, another to Gemini and another to a smaller open model.
In fact, the VB Pulse Foundation Models tracker over the same Q1 period shows that multi-model strategy is the enterprise consensus: respondents increasingly report adopting multiple models and building orchestration layers that route across them by task, cost and risk profile.
An agent runtime is different. Once a company’s workflows, tool permissions, credentials, audit logs, memory, sandboxed execution and operational monitoring live inside one provider’s environment, switching providers becomes less like changing models and more like changing infrastructure.
That is the real reason Anthropic’s 5.7% foothold is worth watching
Anthropic has already made clear that it wants to provide more than the model. Its Claude Managed Agents documentation describes a public beta for a managed agent harness with secure sandboxing, built-in tools and API-run sessions, while Anthropic’s engineering post frames the architecture around decoupling the model from the surrounding agent machinery: the session, the harness and the sandbox.
In plain English, Anthropic is trying to host the environment where Claude agents remember context, use tools, run code, operate inside sandboxes and persist across long-running workflows. That is no longer just inference. That is operational infrastructure.
The pitch is obvious: most enterprises do not want to stitch together their own agent stack from scratch. They want agents that can act, but they also want permission boundaries, audit trails, workflow reliability and ways to stop the system when something goes wrong.
Security is becoming the buying criterion
The VB Pulse orchestration tracker shows that buyers are prioritizing exactly those concerns. Security and permissions ranked as the top orchestration platform selection criterion in both January and February, at 39.3% and 37.1%.
Control over agent execution rose from 17.9% to 22.9%, while flexibility across models and tools fell from 35.7% to 25.7%. The market appears to be shifting from optionality toward governance.
That shift is not surprising. A chatbot can be wrong and still remain mostly contained. An agent that can send emails, modify documents, query databases, call APIs or execute workflows has a much larger blast radius. The enterprise question is not only whether the agent is smart enough.
It is who gave it permission, what it touched, what it changed, whether those actions were logged, and whether the company can unwind the damage if something goes wrong.
Ev Kontsevoy, cofounder and CEO of Teleport, an identity and digital infrastructure solutions company, argues that the industry is still putting too much emphasis on orchestration itself and not enough on identity: “The race to own the agent orchestration layer is real,” Kontsevoy said. “It’s also solving the wrong problem first. Orchestration without identity only multiplies chaos. Without identity, you don’t know what an agent can access, what it actually did, or how to revoke its access when it operates outside policy. A unified identity layer is a prerequisite to deploying agents — one or many — in infrastructure.”
Syam Nair, Chief Product Officer at the enterprise unified data storage solutions firm NetApp, believes data management is key in all cases to secure AI agent orchestration across the enterprise. As he said in a statement to VentureBeat: «Effective agent management requires built-in intelligence and a continuously updated understanding of both data and, critically, its metadata. This visibility allows organizations to define and enforce clear policies so data is used only by the right agents, for the right purposes. Making this work at scale is a crossfunctional effort. Security, storage, and data science teams must work together to implement policies that safeguard company data, while creating a strong data foundation for AI.»
He continued: «The CIOs and technology leaders that are successful are the ones who take the input, policies, and vision from all these teams into account as they build a data infrastructure that minimizes risk and drives business value.»
Microsoft has the distribution edge
That is why Microsoft’s early lead makes sense. Copilot Studio and Azure AI Studio sit inside an enterprise stack many companies already use: Microsoft 365, Teams, Entra ID, Azure and existing procurement relationships.
The VB Pulse Orchestration Tracker for Q1 2026 describes Microsoft as the enterprise default, with no other platform within 13 percentage points in February.
David Weston, CVP, AI Security, Microsoft, provided some insight on why, writing in a statement to VentureBeat: «Without a unified control layer, you start to see fragmentation – agents operating in silos, inconsistent governance, and gaps in security. What customers are asking for is a way to bring order to that complexity. With Agent 365, we’re providing a single control plane to observe, govern, and secure agents across Microsoft, partner, and third-party ecosystems, all grounded in enterprise data and identity.»
OpenAI’s second-place position is also unsurprising. Its Assistants and Responses API gave developers an early way to build agent-like systems using OpenAI’s models and tooling. In the orchestration tracker, OpenAI is not surging, but it is still ticking up steadily: 23.2% in January to 25.7% in February.
Anthropic is the newcomer at the orchestration layer. But its timing may be favorable. The VB Pulse Foundation Models tracker for Q1 2026 suggests enterprises increasingly see Claude as a fit for higher-stakes workloads where safety, instruction following, long context and governance matter.
The orchestration tracker suggests those same buyers are now moving from agent experiments toward production workflows, where security, permissions and task reliability become the gating issues.
That creates a possible path for Anthropic: not to beat Microsoft as the default enterprise platform, at least not immediately, but to become the agent runtime for companies that already trust Claude for sensitive or complex workloads.
The risk is lock-in
The risk for enterprises is lock-in.
The orchestration tracker found that a hybrid control plane — combining provider-native orchestration with external orchestration — was the leading expected architecture, holding around 35% to 36% across the two substantive waves.
Provider-managed-only approaches grew modestly but remained a minority. The report’s conclusion is blunt: enterprises are not willing to give full orchestration control to any single provider.
It makes total sense as enterprises seek to leverage the «best-in-breed» models, harnesses, and tools from multiple vendors, especially as their needs differ widely across sector, business, and size.
«Most enterprises will operate in a multi-model, multi-agent environment, which makes an independent control plane essential,» agreed Felix Van de Maele, CEO of Collibra, a unified data governance startup for AI, in a statement to VentureBeat. «That is why we built AI Command Center: to give organizations the visibility, governance, and real-time oversight needed to manage AI systems and agents across the full lifecycle.»
That caution shows up in the risk data. When asked about risks if agent control lives inside a model provider platform, respondents cited security and permissioning limitations as the top concern. Vendor lock-in was the second-largest concern and the only one that increased from January to February, rising from 23.2% to 25.7%.
This is the tension at the heart of the agent market. Enterprises want managed infrastructure because building reliable agents is hard. But the more a provider manages, the more it may own.
Dr. Rania Khalaf, chief AI officer at WSO2 — the subsidiary of EQT that offers open source, customizable AI stacks for enterprises — said enterprises will need an agent control plane that sits apart from individual frameworks, harnesses and runtimes because agents combine the unpredictability of LLMs with the ability to take actions that have consequences.
“Teams want the freedom to use the best model and framework for each job — Claude for coding, Gemini for writing, LangGraph or CrewAI for dynamic modular behavior — and that heterogeneity makes consistent governance untenable in integrated platforms that lock into one ecosystem,” Khalaf said.
From LLMOps to Agent Ops
Khalaf said the industry is also moving from MLOps to LLMOps to “Agent Ops,” where governance has to cover the whole agent, not just the model call.
“A guardrail on an LLM call can catch hallucination or toxic output, but it will not catch an agent thrashing in an unbreakable, costly loop, which is why governance now has to extend out from the LLM interaction to the scope of the agent,” she said.
The practical implication is that enterprises need to separate policy and control from the agent logic itself. Khalaf pointed to the recent example of an agent deleting a production database despite being told not to, arguing that the failure showed the limits of relying on prompt-level instructions where hard identity and access controls are needed.
“Pulling guardrails, evals, policies, bindings, and agent identity out of the core agent logic allows them to be configured per deployment and per environment, owned by the appropriate teams in security, product, and compliance, without fragmenting the governance layer as different teams choose different models and frameworks,” Khalaf said.
MCP is open. The runtime may still be sticky
That is where Anthropic’s Model Context Protocol, or MCP, complicates the story. MCP is not a walled garden; Anthropic introduced it as an open standard for connecting AI systems to data and tools, and Anthropic’s documentation describes MCP as an open-source standard for connecting AI applications to external systems.
But openness at the protocol layer does not automatically eliminate lock-in at the runtime layer. An enterprise could use an open protocol to connect tools while still becoming dependent on a provider’s managed sessions, logs, sandboxes, permissions model, workflow state and deployment environment. In other words, MCP may reduce integration friction, while managed agent infrastructure could still increase switching costs.
Khalaf said Microsoft’s lead likely reflects its M365 and Azure distribution, while Anthropic’s emerging foothold could reflect a different architectural bet around open protocols such as MCP. But she argued the long-term direction is not a single-provider stack.
“Enterprises serious about running agents in production will end up multi-vendor across these layers,” Khalaf said, “which is why the open and interoperable control plane matters more than the current percentages might suggest.”
The next cycle may be cross-vendor collaboration
That same tension — between provider-native convenience and cross-vendor reality — is where Arick Goomanovsky, CEO and cofounder of universal AI agent orchestrator startup BAND, sees the next competitive cycle forming.
“Enterprises now run agents everywhere: individual assistants and coding agents, multi-agent systems in production, agents embedded in Agentforce and ServiceNow, and third-party agents consumed as agent-as-a-service,” Goomanovsky said. “None of them collaborate across those boundaries by default.”
Goomanovsky argues that the missing layer is not just orchestration inside a single model provider, but a cross-vendor collaboration layer that lets agents from different ecosystems act together.
“What’s emerging in parallel is demand for an agentic collaboration harness – an interaction layer that lets agents from Microsoft, OpenAI, Anthropic, and internal teams operate as one workforce,” he said. “Orchestration inside any single vendor is still a walled garden so the next competitive cycle is cross-vendor agent collaboration.”
Independent frameworks face an enterprise packaging problem
There is also a warning sign for independent orchestration frameworks. LangChain and LangGraph fell from 5.4% to 1.4% as the primary orchestration platform in the qualified enterprise sample.
External orchestration abstracted entirely from model providers also fell from 8.9% to 2.9%.
Scott Likens, Global Chief AI Engineer at professional services giant PwC, has a front row seat to this trend as the company spearheads and assists clients with their AI transformations.
As he told VentureBeat in a statement: «Right now, most enterprises are still operating in fragmented environments, with orchestration spread across platforms, business applications, and internally developed tooling. Over time, the market will likely move toward more unified orchestration models, but interoperability, governance and security will remain critical because enterprises are unlikely to standardize on a single agent ecosystem.»
The report argues that fully independent orchestration frameworks may not yet have the enterprise packaging — security certifications, support, compliance documentation and vendor accountability — that procurement teams require.
That does not mean open frameworks are irrelevant. It does suggest that enterprise buyers may increasingly consume open or developer-first orchestration through managed products, cloud-provider partnerships or internal control planes rather than as standalone frameworks.
The agent market starts to look like cloud infrastructure
This is where the agent market starts to look less like the early chatbot market and more like enterprise cloud infrastructure. The winning vendors will not only have capable models. They will have identity integration, permission controls, audit logs, observability, workflow tooling, sandboxing, evaluation and a credible answer to who owns the control plane.
Indeed, the orchestration layer is but one part of the stack that the enterprise must fill in, and enterprises may actually decide to have different orchestration layers for agents working in different departments and functions.
As Nithya Lakshmanan, Chief Product Officer at revenue team AI orchestration startup Outreach.ai wrote in a statement to VentureBeat: «General-purpose orchestration platforms coordinate agent activity well, but they don’t carry the workflow-specific context that determines whether an agent’s action is correct for a given situation. In revenue workflows, an agent acting on incomplete deal history or missing buyer context will underperform and erode trust with users. The teams getting the most out of multi-agent systems are treating domain-specific data as the governance layer, with orchestration sitting on top. Most enterprises have chosen their orchestration stack, and what they’re now figuring out is how those platforms get access to the workflow context they need to make agents useful inside specific business functions.»
That is why Anthropic — which is increasingly launching its own domain-specific agents for finance and design, among other categories — is worth following closely. The company does not need to win the entire orchestration market tomorrow for its strategy to matter. It only needs to persuade a growing set of Claude enterprise customers to let Anthropic handle more of the surrounding machinery: tools, workflows, memory, execution and governance.
If it succeeds, Claude becomes more than a model in a multi-model portfolio. It becomes part of the infrastructure where enterprise work gets done.
That would put Anthropic in a more direct fight with OpenAI and Microsoft — not just over model quality, but over the operating layer of AI agents.
The narrow but important read
The safe interpretation of the VB Pulse data is narrow but important: Anthropic is not yet a major enterprise orchestration platform. Microsoft is. OpenAI is much closer. But Anthropic has registered its first measurable foothold at the orchestration layer, just as the market is deciding who should control agent execution.
For enterprise buyers, that may be the question that matters most in 2026. Not which model is best, but which provider gets to run the agent — and how hard it will be to leave once the agent is running.
New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI agents run.
For the last two years, the enterprise AI race has mostly been framed as a model war: OpenAI’s GPT series versus Anthropic’s Claude versus Google’s Gemini, with smaller and open-source alternatives also coming in from the U.S. and China.
But the next strategic fight may not be over which model answers a prompt best. It may be over who controls the layer where agents plan, call tools, access data, run workflows and prove to security teams that they did not do anything they were not supposed to do.
New VB Pulse survey data suggests the category is already taking shape. Our independent Enterprise Agentic Orchestration tracker, a survey that records the preferences of qualified, verified technical-decision maker respondents at enterprises at regular intervals, found that Microsoft Copilot Studio and Azure AI Studio led with 38.6% primary-platform adoption in February, up from 35.7% in January.
OpenAI’s Assistants and Responses API held second place, rising from 23.2% to 25.7%.
Anthropic remained far smaller, but it made its first appearance in the tracker: moving from 0% in January to 5.7% in February for Anthropic tool use and workflows.
The underlying move is small — four respondents out of a total 70 in this cohort, with more to come — but strategically interesting because it marks the first sign in this tracker of Claude usage moving from the model layer into native orchestration.
That distinction matters. Enterprises are not merely choosing chatbots. They are deciding where the live operational machinery of AI work will sit: inside Microsoft’s stack, inside OpenAI’s API layer, inside Anthropic’s managed runtime, inside an open framework, or across a hybrid mix of all of them.
“This is the convergence moment for enterprise AI,” said Tom Findling, CEO and cofounder of AI cybsersecurity startup Conifers, in a statement to VentureBeat. “Models and agent frameworks have matured enough together that enterprises are now shifting focus beyond model quality to the control plane around it. In security operations, we’re seeing the competitive advantage move toward platforms that can orchestrate agents, leverage enterprise context, and provide governance and auditability across customer environments.”
Anthropic’s number is still small to start — but the increase is not
The Anthropic number, by itself, should not be overread. A move from zero to 5.7% is not a juggernaut. It is not proof that Anthropic has captured enterprise orchestration.
It is not even enough to say Anthropic has a durable lead in any part of this market. Microsoft owns the early enterprise distribution advantage, and OpenAI has a much larger installed base in orchestration than Anthropic.
But small numbers can matter when they appear at the start of a new market structure. Anthropic’s emergence in orchestration comes as the broader VB Pulse data shows Claude also gaining massive enterprise adoption at the model layer.
In our VB Pulse Q1 Foundation Models and Intelligence Platforms tracker, Anthropic rose from 23.9% in January to 28.6% in February and then even more dramatically to 56.2% in March among qualified enterprise respondents, with the March reading flagged as directional only, because the sample was only 16 respondents.
The story, then, is not that Anthropic is winning orchestration today. It is that Anthropic’s model momentum may be starting to spill into the orchestration layer.
That is where the strategic stakes get higher.
A model is easier to swap than an agent runtime
A model is relatively easy to swap, at least in theory. A company can route one workload to Claude, another to GPT, another to Gemini and another to a smaller open model.
In fact, the VB Pulse Foundation Models tracker over the same Q1 period shows that multi-model strategy is the enterprise consensus: respondents increasingly report adopting multiple models and building orchestration layers that route across them by task, cost and risk profile.
An agent runtime is different. Once a company’s workflows, tool permissions, credentials, audit logs, memory, sandboxed execution and operational monitoring live inside one provider’s environment, switching providers becomes less like changing models and more like changing infrastructure.
That is the real reason Anthropic’s 5.7% foothold is worth watching
Anthropic has already made clear that it wants to provide more than the model. Its Claude Managed Agents documentation describes a public beta for a managed agent harness with secure sandboxing, built-in tools and API-run sessions, while Anthropic’s engineering post frames the architecture around decoupling the model from the surrounding agent machinery: the session, the harness and the sandbox.
In plain English, Anthropic is trying to host the environment where Claude agents remember context, use tools, run code, operate inside sandboxes and persist across long-running workflows. That is no longer just inference. That is operational infrastructure.
The pitch is obvious: most enterprises do not want to stitch together their own agent stack from scratch. They want agents that can act, but they also want permission boundaries, audit trails, workflow reliability and ways to stop the system when something goes wrong.
Security is becoming the buying criterion
The VB Pulse orchestration tracker shows that buyers are prioritizing exactly those concerns. Security and permissions ranked as the top orchestration platform selection criterion in both January and February, at 39.3% and 37.1%.
Control over agent execution rose from 17.9% to 22.9%, while flexibility across models and tools fell from 35.7% to 25.7%. The market appears to be shifting from optionality toward governance.
That shift is not surprising. A chatbot can be wrong and still remain mostly contained. An agent that can send emails, modify documents, query databases, call APIs or execute workflows has a much larger blast radius. The enterprise question is not only whether the agent is smart enough.
It is who gave it permission, what it touched, what it changed, whether those actions were logged, and whether the company can unwind the damage if something goes wrong.
Ev Kontsevoy, cofounder and CEO of Teleport, an identity and digital infrastructure solutions company, argues that the industry is still putting too much emphasis on orchestration itself and not enough on identity: “The race to own the agent orchestration layer is real,” Kontsevoy said. “It’s also solving the wrong problem first. Orchestration without identity only multiplies chaos. Without identity, you don’t know what an agent can access, what it actually did, or how to revoke its access when it operates outside policy. A unified identity layer is a prerequisite to deploying agents — one or many — in infrastructure.”
Syam Nair, Chief Product Officer at the enterprise unified data storage solutions firm NetApp, believes data management is key in all cases to secure AI agent orchestration across the enterprise. As he said in a statement to VentureBeat: «Effective agent management requires built-in intelligence and a continuously updated understanding of both data and, critically, its metadata. This visibility allows organizations to define and enforce clear policies so data is used only by the right agents, for the right purposes. Making this work at scale is a crossfunctional effort. Security, storage, and data science teams must work together to implement policies that safeguard company data, while creating a strong data foundation for AI.»
He continued: «The CIOs and technology leaders that are successful are the ones who take the input, policies, and vision from all these teams into account as they build a data infrastructure that minimizes risk and drives business value.»
Microsoft has the distribution edge
That is why Microsoft’s early lead makes sense. Copilot Studio and Azure AI Studio sit inside an enterprise stack many companies already use: Microsoft 365, Teams, Entra ID, Azure and existing procurement relationships.
The VB Pulse Orchestration Tracker for Q1 2026 describes Microsoft as the enterprise default, with no other platform within 13 percentage points in February.
David Weston, CVP, AI Security, Microsoft, provided some insight on why, writing in a statement to VentureBeat: «Without a unified control layer, you start to see fragmentation – agents operating in silos, inconsistent governance, and gaps in security. What customers are asking for is a way to bring order to that complexity. With Agent 365, we’re providing a single control plane to observe, govern, and secure agents across Microsoft, partner, and third-party ecosystems, all grounded in enterprise data and identity.»
OpenAI’s second-place position is also unsurprising. Its Assistants and Responses API gave developers an early way to build agent-like systems using OpenAI’s models and tooling. In the orchestration tracker, OpenAI is not surging, but it is still ticking up steadily: 23.2% in January to 25.7% in February.
Anthropic is the newcomer at the orchestration layer. But its timing may be favorable. The VB Pulse Foundation Models tracker for Q1 2026 suggests enterprises increasingly see Claude as a fit for higher-stakes workloads where safety, instruction following, long context and governance matter.
The orchestration tracker suggests those same buyers are now moving from agent experiments toward production workflows, where security, permissions and task reliability become the gating issues.
That creates a possible path for Anthropic: not to beat Microsoft as the default enterprise platform, at least not immediately, but to become the agent runtime for companies that already trust Claude for sensitive or complex workloads.
The risk is lock-in
The risk for enterprises is lock-in.
The orchestration tracker found that a hybrid control plane — combining provider-native orchestration with external orchestration — was the leading expected architecture, holding around 35% to 36% across the two substantive waves.
Provider-managed-only approaches grew modestly but remained a minority. The report’s conclusion is blunt: enterprises are not willing to give full orchestration control to any single provider.
It makes total sense as enterprises seek to leverage the «best-in-breed» models, harnesses, and tools from multiple vendors, especially as their needs differ widely across sector, business, and size.
«Most enterprises will operate in a multi-model, multi-agent environment, which makes an independent control plane essential,» agreed Felix Van de Maele, CEO of Collibra, a unified data governance startup for AI, in a statement to VentureBeat. «That is why we built AI Command Center: to give organizations the visibility, governance, and real-time oversight needed to manage AI systems and agents across the full lifecycle.»
That caution shows up in the risk data. When asked about risks if agent control lives inside a model provider platform, respondents cited security and permissioning limitations as the top concern. Vendor lock-in was the second-largest concern and the only one that increased from January to February, rising from 23.2% to 25.7%.
This is the tension at the heart of the agent market. Enterprises want managed infrastructure because building reliable agents is hard. But the more a provider manages, the more it may own.
Dr. Rania Khalaf, chief AI officer at WSO2 — the subsidiary of EQT that offers open source, customizable AI stacks for enterprises — said enterprises will need an agent control plane that sits apart from individual frameworks, harnesses and runtimes because agents combine the unpredictability of LLMs with the ability to take actions that have consequences.
“Teams want the freedom to use the best model and framework for each job — Claude for coding, Gemini for writing, LangGraph or CrewAI for dynamic modular behavior — and that heterogeneity makes consistent governance untenable in integrated platforms that lock into one ecosystem,” Khalaf said.
From LLMOps to Agent Ops
Khalaf said the industry is also moving from MLOps to LLMOps to “Agent Ops,” where governance has to cover the whole agent, not just the model call.
“A guardrail on an LLM call can catch hallucination or toxic output, but it will not catch an agent thrashing in an unbreakable, costly loop, which is why governance now has to extend out from the LLM interaction to the scope of the agent,” she said.
The practical implication is that enterprises need to separate policy and control from the agent logic itself. Khalaf pointed to the recent example of an agent deleting a production database despite being told not to, arguing that the failure showed the limits of relying on prompt-level instructions where hard identity and access controls are needed.
“Pulling guardrails, evals, policies, bindings, and agent identity out of the core agent logic allows them to be configured per deployment and per environment, owned by the appropriate teams in security, product, and compliance, without fragmenting the governance layer as different teams choose different models and frameworks,” Khalaf said.
MCP is open. The runtime may still be sticky
That is where Anthropic’s Model Context Protocol, or MCP, complicates the story. MCP is not a walled garden; Anthropic introduced it as an open standard for connecting AI systems to data and tools, and Anthropic’s documentation describes MCP as an open-source standard for connecting AI applications to external systems.
But openness at the protocol layer does not automatically eliminate lock-in at the runtime layer. An enterprise could use an open protocol to connect tools while still becoming dependent on a provider’s managed sessions, logs, sandboxes, permissions model, workflow state and deployment environment. In other words, MCP may reduce integration friction, while managed agent infrastructure could still increase switching costs.
Khalaf said Microsoft’s lead likely reflects its M365 and Azure distribution, while Anthropic’s emerging foothold could reflect a different architectural bet around open protocols such as MCP. But she argued the long-term direction is not a single-provider stack.
“Enterprises serious about running agents in production will end up multi-vendor across these layers,” Khalaf said, “which is why the open and interoperable control plane matters more than the current percentages might suggest.”
The next cycle may be cross-vendor collaboration
That same tension — between provider-native convenience and cross-vendor reality — is where Arick Goomanovsky, CEO and cofounder of universal AI agent orchestrator startup BAND, sees the next competitive cycle forming.
“Enterprises now run agents everywhere: individual assistants and coding agents, multi-agent systems in production, agents embedded in Agentforce and ServiceNow, and third-party agents consumed as agent-as-a-service,” Goomanovsky said. “None of them collaborate across those boundaries by default.”
Goomanovsky argues that the missing layer is not just orchestration inside a single model provider, but a cross-vendor collaboration layer that lets agents from different ecosystems act together.
“What’s emerging in parallel is demand for an agentic collaboration harness – an interaction layer that lets agents from Microsoft, OpenAI, Anthropic, and internal teams operate as one workforce,” he said. “Orchestration inside any single vendor is still a walled garden so the next competitive cycle is cross-vendor agent collaboration.”
Independent frameworks face an enterprise packaging problem
There is also a warning sign for independent orchestration frameworks. LangChain and LangGraph fell from 5.4% to 1.4% as the primary orchestration platform in the qualified enterprise sample.
External orchestration abstracted entirely from model providers also fell from 8.9% to 2.9%.
Scott Likens, Global Chief AI Engineer at professional services giant PwC, has a front row seat to this trend as the company spearheads and assists clients with their AI transformations.
As he told VentureBeat in a statement: «Right now, most enterprises are still operating in fragmented environments, with orchestration spread across platforms, business applications, and internally developed tooling. Over time, the market will likely move toward more unified orchestration models, but interoperability, governance and security will remain critical because enterprises are unlikely to standardize on a single agent ecosystem.»
The report argues that fully independent orchestration frameworks may not yet have the enterprise packaging — security certifications, support, compliance documentation and vendor accountability — that procurement teams require.
That does not mean open frameworks are irrelevant. It does suggest that enterprise buyers may increasingly consume open or developer-first orchestration through managed products, cloud-provider partnerships or internal control planes rather than as standalone frameworks.
The agent market starts to look like cloud infrastructure
This is where the agent market starts to look less like the early chatbot market and more like enterprise cloud infrastructure. The winning vendors will not only have capable models. They will have identity integration, permission controls, audit logs, observability, workflow tooling, sandboxing, evaluation and a credible answer to who owns the control plane.
Indeed, the orchestration layer is but one part of the stack that the enterprise must fill in, and enterprises may actually decide to have different orchestration layers for agents working in different departments and functions.
As Nithya Lakshmanan, Chief Product Officer at revenue team AI orchestration startup Outreach.ai wrote in a statement to VentureBeat: «General-purpose orchestration platforms coordinate agent activity well, but they don’t carry the workflow-specific context that determines whether an agent’s action is correct for a given situation. In revenue workflows, an agent acting on incomplete deal history or missing buyer context will underperform and erode trust with users. The teams getting the most out of multi-agent systems are treating domain-specific data as the governance layer, with orchestration sitting on top. Most enterprises have chosen their orchestration stack, and what they’re now figuring out is how those platforms get access to the workflow context they need to make agents useful inside specific business functions.»
That is why Anthropic — which is increasingly launching its own domain-specific agents for finance and design, among other categories — is worth following closely. The company does not need to win the entire orchestration market tomorrow for its strategy to matter. It only needs to persuade a growing set of Claude enterprise customers to let Anthropic handle more of the surrounding machinery: tools, workflows, memory, execution and governance.
If it succeeds, Claude becomes more than a model in a multi-model portfolio. It becomes part of the infrastructure where enterprise work gets done.
That would put Anthropic in a more direct fight with OpenAI and Microsoft — not just over model quality, but over the operating layer of AI agents.
The narrow but important read
The safe interpretation of the VB Pulse data is narrow but important: Anthropic is not yet a major enterprise orchestration platform. Microsoft is. OpenAI is much closer. But Anthropic has registered its first measurable foothold at the orchestration layer, just as the market is deciding who should control agent execution.
For enterprise buyers, that may be the question that matters most in 2026. Not which model is best, but which provider gets to run the agent — and how hard it will be to leave once the agent is running.
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New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI agents run.
For the last two years, the enterprise AI race has mostly been framed as a model war: OpenAI’s GPT series versus Anthropic’s Claude versus Google’s Gemini, with smaller and open-source alternatives also coming in from the U.S. and China.
But the next strategic fight may not be over which model answers a prompt best. It may be over who controls the layer where agents plan, call tools, access data, run workflows and prove to security teams that they did not do anything they were not supposed to do.
New VB Pulse survey data suggests the category is already taking shape. Our independent Enterprise Agentic Orchestration tracker, a survey that records the preferences of qualified, verified technical-decision maker respondents at enterprises at regular intervals, found that Microsoft Copilot Studio and Azure AI Studio led with 38.6% primary-platform adoption in February, up from 35.7% in January.
OpenAI’s Assistants and Responses API held second place, rising from 23.2% to 25.7%.
Anthropic remained far smaller, but it made its first appearance in the tracker: moving from 0% in January to 5.7% in February for Anthropic tool use and workflows.
The underlying move is small — four respondents out of a total 70 in this cohort, with more to come — but strategically interesting because it marks the first sign in this tracker of Claude usage moving from the model layer into native orchestration.
That distinction matters. Enterprises are not merely choosing chatbots. They are deciding where the live operational machinery of AI work will sit: inside Microsoft’s stack, inside OpenAI’s API layer, inside Anthropic’s managed runtime, inside an open framework, or across a hybrid mix of all of them.
“This is the convergence moment for enterprise AI,” said Tom Findling, CEO and cofounder of AI cybsersecurity startup Conifers, in a statement to VentureBeat. “Models and agent frameworks have matured enough together that enterprises are now shifting focus beyond model quality to the control plane around it. In security operations, we’re seeing the competitive advantage move toward platforms that can orchestrate agents, leverage enterprise context, and provide governance and auditability across customer environments.”
Anthropic’s number is still small to start — but the increase is not
The Anthropic number, by itself, should not be overread. A move from zero to 5.7% is not a juggernaut. It is not proof that Anthropic has captured enterprise orchestration.
It is not even enough to say Anthropic has a durable lead in any part of this market. Microsoft owns the early enterprise distribution advantage, and OpenAI has a much larger installed base in orchestration than Anthropic.
But small numbers can matter when they appear at the start of a new market structure. Anthropic’s emergence in orchestration comes as the broader VB Pulse data shows Claude also gaining massive enterprise adoption at the model layer.
In our VB Pulse Q1 Foundation Models and Intelligence Platforms tracker, Anthropic rose from 23.9% in January to 28.6% in February and then even more dramatically to 56.2% in March among qualified enterprise respondents, with the March reading flagged as directional only, because the sample was only 16 respondents.
The story, then, is not that Anthropic is winning orchestration today. It is that Anthropic’s model momentum may be starting to spill into the orchestration layer.
That is where the strategic stakes get higher.
A model is easier to swap than an agent runtime
A model is relatively easy to swap, at least in theory. A company can route one workload to Claude, another to GPT, another to Gemini and another to a smaller open model.
In fact, the VB Pulse Foundation Models tracker over the same Q1 period shows that multi-model strategy is the enterprise consensus: respondents increasingly report adopting multiple models and building orchestration layers that route across them by task, cost and risk profile.
An agent runtime is different. Once a company’s workflows, tool permissions, credentials, audit logs, memory, sandboxed execution and operational monitoring live inside one provider’s environment, switching providers becomes less like changing models and more like changing infrastructure.
That is the real reason Anthropic’s 5.7% foothold is worth watching
Anthropic has already made clear that it wants to provide more than the model. Its Claude Managed Agents documentation describes a public beta for a managed agent harness with secure sandboxing, built-in tools and API-run sessions, while Anthropic’s engineering post frames the architecture around decoupling the model from the surrounding agent machinery: the session, the harness and the sandbox.
In plain English, Anthropic is trying to host the environment where Claude agents remember context, use tools, run code, operate inside sandboxes and persist across long-running workflows. That is no longer just inference. That is operational infrastructure.
The pitch is obvious: most enterprises do not want to stitch together their own agent stack from scratch. They want agents that can act, but they also want permission boundaries, audit trails, workflow reliability and ways to stop the system when something goes wrong.
Security is becoming the buying criterion
The VB Pulse orchestration tracker shows that buyers are prioritizing exactly those concerns. Security and permissions ranked as the top orchestration platform selection criterion in both January and February, at 39.3% and 37.1%.
Control over agent execution rose from 17.9% to 22.9%, while flexibility across models and tools fell from 35.7% to 25.7%. The market appears to be shifting from optionality toward governance.
That shift is not surprising. A chatbot can be wrong and still remain mostly contained. An agent that can send emails, modify documents, query databases, call APIs or execute workflows has a much larger blast radius. The enterprise question is not only whether the agent is smart enough.
It is who gave it permission, what it touched, what it changed, whether those actions were logged, and whether the company can unwind the damage if something goes wrong.
Ev Kontsevoy, cofounder and CEO of Teleport, an identity and digital infrastructure solutions company, argues that the industry is still putting too much emphasis on orchestration itself and not enough on identity: “The race to own the agent orchestration layer is real,” Kontsevoy said. “It’s also solving the wrong problem first. Orchestration without identity only multiplies chaos. Without identity, you don’t know what an agent can access, what it actually did, or how to revoke its access when it operates outside policy. A unified identity layer is a prerequisite to deploying agents — one or many — in infrastructure.”
Syam Nair, Chief Product Officer at the enterprise unified data storage solutions firm NetApp, believes data management is key in all cases to secure AI agent orchestration across the enterprise. As he said in a statement to VentureBeat: «Effective agent management requires built-in intelligence and a continuously updated understanding of both data and, critically, its metadata. This visibility allows organizations to define and enforce clear policies so data is used only by the right agents, for the right purposes. Making this work at scale is a crossfunctional effort. Security, storage, and data science teams must work together to implement policies that safeguard company data, while creating a strong data foundation for AI.»
He continued: «The CIOs and technology leaders that are successful are the ones who take the input, policies, and vision from all these teams into account as they build a data infrastructure that minimizes risk and drives business value.»
Microsoft has the distribution edge
That is why Microsoft’s early lead makes sense. Copilot Studio and Azure AI Studio sit inside an enterprise stack many companies already use: Microsoft 365, Teams, Entra ID, Azure and existing procurement relationships.
The VB Pulse Orchestration Tracker for Q1 2026 describes Microsoft as the enterprise default, with no other platform within 13 percentage points in February.
David Weston, CVP, AI Security, Microsoft, provided some insight on why, writing in a statement to VentureBeat: «Without a unified control layer, you start to see fragmentation – agents operating in silos, inconsistent governance, and gaps in security. What customers are asking for is a way to bring order to that complexity. With Agent 365, we’re providing a single control plane to observe, govern, and secure agents across Microsoft, partner, and third-party ecosystems, all grounded in enterprise data and identity.»
OpenAI’s second-place position is also unsurprising. Its Assistants and Responses API gave developers an early way to build agent-like systems using OpenAI’s models and tooling. In the orchestration tracker, OpenAI is not surging, but it is still ticking up steadily: 23.2% in January to 25.7% in February.
Anthropic is the newcomer at the orchestration layer. But its timing may be favorable. The VB Pulse Foundation Models tracker for Q1 2026 suggests enterprises increasingly see Claude as a fit for higher-stakes workloads where safety, instruction following, long context and governance matter.
The orchestration tracker suggests those same buyers are now moving from agent experiments toward production workflows, where security, permissions and task reliability become the gating issues.
That creates a possible path for Anthropic: not to beat Microsoft as the default enterprise platform, at least not immediately, but to become the agent runtime for companies that already trust Claude for sensitive or complex workloads.
The risk is lock-in
The risk for enterprises is lock-in.
The orchestration tracker found that a hybrid control plane — combining provider-native orchestration with external orchestration — was the leading expected architecture, holding around 35% to 36% across the two substantive waves.
Provider-managed-only approaches grew modestly but remained a minority. The report’s conclusion is blunt: enterprises are not willing to give full orchestration control to any single provider.
It makes total sense as enterprises seek to leverage the «best-in-breed» models, harnesses, and tools from multiple vendors, especially as their needs differ widely across sector, business, and size.
«Most enterprises will operate in a multi-model, multi-agent environment, which makes an independent control plane essential,» agreed Felix Van de Maele, CEO of Collibra, a unified data governance startup for AI, in a statement to VentureBeat. «That is why we built AI Command Center: to give organizations the visibility, governance, and real-time oversight needed to manage AI systems and agents across the full lifecycle.»
That caution shows up in the risk data. When asked about risks if agent control lives inside a model provider platform, respondents cited security and permissioning limitations as the top concern. Vendor lock-in was the second-largest concern and the only one that increased from January to February, rising from 23.2% to 25.7%.
This is the tension at the heart of the agent market. Enterprises want managed infrastructure because building reliable agents is hard. But the more a provider manages, the more it may own.
Dr. Rania Khalaf, chief AI officer at WSO2 — the subsidiary of EQT that offers open source, customizable AI stacks for enterprises — said enterprises will need an agent control plane that sits apart from individual frameworks, harnesses and runtimes because agents combine the unpredictability of LLMs with the ability to take actions that have consequences.
“Teams want the freedom to use the best model and framework for each job — Claude for coding, Gemini for writing, LangGraph or CrewAI for dynamic modular behavior — and that heterogeneity makes consistent governance untenable in integrated platforms that lock into one ecosystem,” Khalaf said.
From LLMOps to Agent Ops
Khalaf said the industry is also moving from MLOps to LLMOps to “Agent Ops,” where governance has to cover the whole agent, not just the model call.
“A guardrail on an LLM call can catch hallucination or toxic output, but it will not catch an agent thrashing in an unbreakable, costly loop, which is why governance now has to extend out from the LLM interaction to the scope of the agent,” she said.
The practical implication is that enterprises need to separate policy and control from the agent logic itself. Khalaf pointed to the recent example of an agent deleting a production database despite being told not to, arguing that the failure showed the limits of relying on prompt-level instructions where hard identity and access controls are needed.
“Pulling guardrails, evals, policies, bindings, and agent identity out of the core agent logic allows them to be configured per deployment and per environment, owned by the appropriate teams in security, product, and compliance, without fragmenting the governance layer as different teams choose different models and frameworks,” Khalaf said.
MCP is open. The runtime may still be sticky
That is where Anthropic’s Model Context Protocol, or MCP, complicates the story. MCP is not a walled garden; Anthropic introduced it as an open standard for connecting AI systems to data and tools, and Anthropic’s documentation describes MCP as an open-source standard for connecting AI applications to external systems.
But openness at the protocol layer does not automatically eliminate lock-in at the runtime layer. An enterprise could use an open protocol to connect tools while still becoming dependent on a provider’s managed sessions, logs, sandboxes, permissions model, workflow state and deployment environment. In other words, MCP may reduce integration friction, while managed agent infrastructure could still increase switching costs.
Khalaf said Microsoft’s lead likely reflects its M365 and Azure distribution, while Anthropic’s emerging foothold could reflect a different architectural bet around open protocols such as MCP. But she argued the long-term direction is not a single-provider stack.
“Enterprises serious about running agents in production will end up multi-vendor across these layers,” Khalaf said, “which is why the open and interoperable control plane matters more than the current percentages might suggest.”
The next cycle may be cross-vendor collaboration
That same tension — between provider-native convenience and cross-vendor reality — is where Arick Goomanovsky, CEO and cofounder of universal AI agent orchestrator startup BAND, sees the next competitive cycle forming.
“Enterprises now run agents everywhere: individual assistants and coding agents, multi-agent systems in production, agents embedded in Agentforce and ServiceNow, and third-party agents consumed as agent-as-a-service,” Goomanovsky said. “None of them collaborate across those boundaries by default.”
Goomanovsky argues that the missing layer is not just orchestration inside a single model provider, but a cross-vendor collaboration layer that lets agents from different ecosystems act together.
“What’s emerging in parallel is demand for an agentic collaboration harness – an interaction layer that lets agents from Microsoft, OpenAI, Anthropic, and internal teams operate as one workforce,” he said. “Orchestration inside any single vendor is still a walled garden so the next competitive cycle is cross-vendor agent collaboration.”
Independent frameworks face an enterprise packaging problem
There is also a warning sign for independent orchestration frameworks. LangChain and LangGraph fell from 5.4% to 1.4% as the primary orchestration platform in the qualified enterprise sample.
External orchestration abstracted entirely from model providers also fell from 8.9% to 2.9%.
Scott Likens, Global Chief AI Engineer at professional services giant PwC, has a front row seat to this trend as the company spearheads and assists clients with their AI transformations.
As he told VentureBeat in a statement: «Right now, most enterprises are still operating in fragmented environments, with orchestration spread across platforms, business applications, and internally developed tooling. Over time, the market will likely move toward more unified orchestration models, but interoperability, governance and security will remain critical because enterprises are unlikely to standardize on a single agent ecosystem.»
The report argues that fully independent orchestration frameworks may not yet have the enterprise packaging — security certifications, support, compliance documentation and vendor accountability — that procurement teams require.
That does not mean open frameworks are irrelevant. It does suggest that enterprise buyers may increasingly consume open or developer-first orchestration through managed products, cloud-provider partnerships or internal control planes rather than as standalone frameworks.
The agent market starts to look like cloud infrastructure
This is where the agent market starts to look less like the early chatbot market and more like enterprise cloud infrastructure. The winning vendors will not only have capable models. They will have identity integration, permission controls, audit logs, observability, workflow tooling, sandboxing, evaluation and a credible answer to who owns the control plane.
Indeed, the orchestration layer is but one part of the stack that the enterprise must fill in, and enterprises may actually decide to have different orchestration layers for agents working in different departments and functions.
As Nithya Lakshmanan, Chief Product Officer at revenue team AI orchestration startup Outreach.ai wrote in a statement to VentureBeat: «General-purpose orchestration platforms coordinate agent activity well, but they don’t carry the workflow-specific context that determines whether an agent’s action is correct for a given situation. In revenue workflows, an agent acting on incomplete deal history or missing buyer context will underperform and erode trust with users. The teams getting the most out of multi-agent systems are treating domain-specific data as the governance layer, with orchestration sitting on top. Most enterprises have chosen their orchestration stack, and what they’re now figuring out is how those platforms get access to the workflow context they need to make agents useful inside specific business functions.»
That is why Anthropic — which is increasingly launching its own domain-specific agents for finance and design, among other categories — is worth following closely. The company does not need to win the entire orchestration market tomorrow for its strategy to matter. It only needs to persuade a growing set of Claude enterprise customers to let Anthropic handle more of the surrounding machinery: tools, workflows, memory, execution and governance.
If it succeeds, Claude becomes more than a model in a multi-model portfolio. It becomes part of the infrastructure where enterprise work gets done.
That would put Anthropic in a more direct fight with OpenAI and Microsoft — not just over model quality, but over the operating layer of AI agents.
The narrow but important read
The safe interpretation of the VB Pulse data is narrow but important: Anthropic is not yet a major enterprise orchestration platform. Microsoft is. OpenAI is much closer. But Anthropic has registered its first measurable foothold at the orchestration layer, just as the market is deciding who should control agent execution.
For enterprise buyers, that may be the question that matters most in 2026. Not which model is best, but which provider gets to run the agent — and how hard it will be to leave once the agent is running.
New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI agents run.
For the last two years, the enterprise AI race has mostly been framed as a model war: OpenAI’s GPT series versus Anthropic’s Claude versus Google’s Gemini, with smaller and open-source alternatives also coming in from the U.S. and China.
But the next strategic fight may not be over which model answers a prompt best. It may be over who controls the layer where agents plan, call tools, access data, run workflows and prove to security teams that they did not do anything they were not supposed to do.
New VB Pulse survey data suggests the category is already taking shape. Our independent Enterprise Agentic Orchestration tracker, a survey that records the preferences of qualified, verified technical-decision maker respondents at enterprises at regular intervals, found that Microsoft Copilot Studio and Azure AI Studio led with 38.6% primary-platform adoption in February, up from 35.7% in January.
OpenAI’s Assistants and Responses API held second place, rising from 23.2% to 25.7%.
Anthropic remained far smaller, but it made its first appearance in the tracker: moving from 0% in January to 5.7% in February for Anthropic tool use and workflows.
The underlying move is small — four respondents out of a total 70 in this cohort, with more to come — but strategically interesting because it marks the first sign in this tracker of Claude usage moving from the model layer into native orchestration.
That distinction matters. Enterprises are not merely choosing chatbots. They are deciding where the live operational machinery of AI work will sit: inside Microsoft’s stack, inside OpenAI’s API layer, inside Anthropic’s managed runtime, inside an open framework, or across a hybrid mix of all of them.
“This is the convergence moment for enterprise AI,” said Tom Findling, CEO and cofounder of AI cybsersecurity startup Conifers, in a statement to VentureBeat. “Models and agent frameworks have matured enough together that enterprises are now shifting focus beyond model quality to the control plane around it. In security operations, we’re seeing the competitive advantage move toward platforms that can orchestrate agents, leverage enterprise context, and provide governance and auditability across customer environments.”
Anthropic’s number is still small to start — but the increase is not
The Anthropic number, by itself, should not be overread. A move from zero to 5.7% is not a juggernaut. It is not proof that Anthropic has captured enterprise orchestration.
It is not even enough to say Anthropic has a durable lead in any part of this market. Microsoft owns the early enterprise distribution advantage, and OpenAI has a much larger installed base in orchestration than Anthropic.
But small numbers can matter when they appear at the start of a new market structure. Anthropic’s emergence in orchestration comes as the broader VB Pulse data shows Claude also gaining massive enterprise adoption at the model layer.
In our VB Pulse Q1 Foundation Models and Intelligence Platforms tracker, Anthropic rose from 23.9% in January to 28.6% in February and then even more dramatically to 56.2% in March among qualified enterprise respondents, with the March reading flagged as directional only, because the sample was only 16 respondents.
The story, then, is not that Anthropic is winning orchestration today. It is that Anthropic’s model momentum may be starting to spill into the orchestration layer.
That is where the strategic stakes get higher.
A model is easier to swap than an agent runtime
A model is relatively easy to swap, at least in theory. A company can route one workload to Claude, another to GPT, another to Gemini and another to a smaller open model.
In fact, the VB Pulse Foundation Models tracker over the same Q1 period shows that multi-model strategy is the enterprise consensus: respondents increasingly report adopting multiple models and building orchestration layers that route across them by task, cost and risk profile.
An agent runtime is different. Once a company’s workflows, tool permissions, credentials, audit logs, memory, sandboxed execution and operational monitoring live inside one provider’s environment, switching providers becomes less like changing models and more like changing infrastructure.
That is the real reason Anthropic’s 5.7% foothold is worth watching
Anthropic has already made clear that it wants to provide more than the model. Its Claude Managed Agents documentation describes a public beta for a managed agent harness with secure sandboxing, built-in tools and API-run sessions, while Anthropic’s engineering post frames the architecture around decoupling the model from the surrounding agent machinery: the session, the harness and the sandbox.
In plain English, Anthropic is trying to host the environment where Claude agents remember context, use tools, run code, operate inside sandboxes and persist across long-running workflows. That is no longer just inference. That is operational infrastructure.
The pitch is obvious: most enterprises do not want to stitch together their own agent stack from scratch. They want agents that can act, but they also want permission boundaries, audit trails, workflow reliability and ways to stop the system when something goes wrong.
Security is becoming the buying criterion
The VB Pulse orchestration tracker shows that buyers are prioritizing exactly those concerns. Security and permissions ranked as the top orchestration platform selection criterion in both January and February, at 39.3% and 37.1%.
Control over agent execution rose from 17.9% to 22.9%, while flexibility across models and tools fell from 35.7% to 25.7%. The market appears to be shifting from optionality toward governance.
That shift is not surprising. A chatbot can be wrong and still remain mostly contained. An agent that can send emails, modify documents, query databases, call APIs or execute workflows has a much larger blast radius. The enterprise question is not only whether the agent is smart enough.
It is who gave it permission, what it touched, what it changed, whether those actions were logged, and whether the company can unwind the damage if something goes wrong.
Ev Kontsevoy, cofounder and CEO of Teleport, an identity and digital infrastructure solutions company, argues that the industry is still putting too much emphasis on orchestration itself and not enough on identity: “The race to own the agent orchestration layer is real,” Kontsevoy said. “It’s also solving the wrong problem first. Orchestration without identity only multiplies chaos. Without identity, you don’t know what an agent can access, what it actually did, or how to revoke its access when it operates outside policy. A unified identity layer is a prerequisite to deploying agents — one or many — in infrastructure.”
Syam Nair, Chief Product Officer at the enterprise unified data storage solutions firm NetApp, believes data management is key in all cases to secure AI agent orchestration across the enterprise. As he said in a statement to VentureBeat: «Effective agent management requires built-in intelligence and a continuously updated understanding of both data and, critically, its metadata. This visibility allows organizations to define and enforce clear policies so data is used only by the right agents, for the right purposes. Making this work at scale is a crossfunctional effort. Security, storage, and data science teams must work together to implement policies that safeguard company data, while creating a strong data foundation for AI.»
He continued: «The CIOs and technology leaders that are successful are the ones who take the input, policies, and vision from all these teams into account as they build a data infrastructure that minimizes risk and drives business value.»
Microsoft has the distribution edge
That is why Microsoft’s early lead makes sense. Copilot Studio and Azure AI Studio sit inside an enterprise stack many companies already use: Microsoft 365, Teams, Entra ID, Azure and existing procurement relationships.
The VB Pulse Orchestration Tracker for Q1 2026 describes Microsoft as the enterprise default, with no other platform within 13 percentage points in February.
David Weston, CVP, AI Security, Microsoft, provided some insight on why, writing in a statement to VentureBeat: «Without a unified control layer, you start to see fragmentation – agents operating in silos, inconsistent governance, and gaps in security. What customers are asking for is a way to bring order to that complexity. With Agent 365, we’re providing a single control plane to observe, govern, and secure agents across Microsoft, partner, and third-party ecosystems, all grounded in enterprise data and identity.»
OpenAI’s second-place position is also unsurprising. Its Assistants and Responses API gave developers an early way to build agent-like systems using OpenAI’s models and tooling. In the orchestration tracker, OpenAI is not surging, but it is still ticking up steadily: 23.2% in January to 25.7% in February.
Anthropic is the newcomer at the orchestration layer. But its timing may be favorable. The VB Pulse Foundation Models tracker for Q1 2026 suggests enterprises increasingly see Claude as a fit for higher-stakes workloads where safety, instruction following, long context and governance matter.
The orchestration tracker suggests those same buyers are now moving from agent experiments toward production workflows, where security, permissions and task reliability become the gating issues.
That creates a possible path for Anthropic: not to beat Microsoft as the default enterprise platform, at least not immediately, but to become the agent runtime for companies that already trust Claude for sensitive or complex workloads.
The risk is lock-in
The risk for enterprises is lock-in.
The orchestration tracker found that a hybrid control plane — combining provider-native orchestration with external orchestration — was the leading expected architecture, holding around 35% to 36% across the two substantive waves.
Provider-managed-only approaches grew modestly but remained a minority. The report’s conclusion is blunt: enterprises are not willing to give full orchestration control to any single provider.
It makes total sense as enterprises seek to leverage the «best-in-breed» models, harnesses, and tools from multiple vendors, especially as their needs differ widely across sector, business, and size.
«Most enterprises will operate in a multi-model, multi-agent environment, which makes an independent control plane essential,» agreed Felix Van de Maele, CEO of Collibra, a unified data governance startup for AI, in a statement to VentureBeat. «That is why we built AI Command Center: to give organizations the visibility, governance, and real-time oversight needed to manage AI systems and agents across the full lifecycle.»
That caution shows up in the risk data. When asked about risks if agent control lives inside a model provider platform, respondents cited security and permissioning limitations as the top concern. Vendor lock-in was the second-largest concern and the only one that increased from January to February, rising from 23.2% to 25.7%.
This is the tension at the heart of the agent market. Enterprises want managed infrastructure because building reliable agents is hard. But the more a provider manages, the more it may own.
Dr. Rania Khalaf, chief AI officer at WSO2 — the subsidiary of EQT that offers open source, customizable AI stacks for enterprises — said enterprises will need an agent control plane that sits apart from individual frameworks, harnesses and runtimes because agents combine the unpredictability of LLMs with the ability to take actions that have consequences.
“Teams want the freedom to use the best model and framework for each job — Claude for coding, Gemini for writing, LangGraph or CrewAI for dynamic modular behavior — and that heterogeneity makes consistent governance untenable in integrated platforms that lock into one ecosystem,” Khalaf said.
From LLMOps to Agent Ops
Khalaf said the industry is also moving from MLOps to LLMOps to “Agent Ops,” where governance has to cover the whole agent, not just the model call.
“A guardrail on an LLM call can catch hallucination or toxic output, but it will not catch an agent thrashing in an unbreakable, costly loop, which is why governance now has to extend out from the LLM interaction to the scope of the agent,” she said.
The practical implication is that enterprises need to separate policy and control from the agent logic itself. Khalaf pointed to the recent example of an agent deleting a production database despite being told not to, arguing that the failure showed the limits of relying on prompt-level instructions where hard identity and access controls are needed.
“Pulling guardrails, evals, policies, bindings, and agent identity out of the core agent logic allows them to be configured per deployment and per environment, owned by the appropriate teams in security, product, and compliance, without fragmenting the governance layer as different teams choose different models and frameworks,” Khalaf said.
MCP is open. The runtime may still be sticky
That is where Anthropic’s Model Context Protocol, or MCP, complicates the story. MCP is not a walled garden; Anthropic introduced it as an open standard for connecting AI systems to data and tools, and Anthropic’s documentation describes MCP as an open-source standard for connecting AI applications to external systems.
But openness at the protocol layer does not automatically eliminate lock-in at the runtime layer. An enterprise could use an open protocol to connect tools while still becoming dependent on a provider’s managed sessions, logs, sandboxes, permissions model, workflow state and deployment environment. In other words, MCP may reduce integration friction, while managed agent infrastructure could still increase switching costs.
Khalaf said Microsoft’s lead likely reflects its M365 and Azure distribution, while Anthropic’s emerging foothold could reflect a different architectural bet around open protocols such as MCP. But she argued the long-term direction is not a single-provider stack.
“Enterprises serious about running agents in production will end up multi-vendor across these layers,” Khalaf said, “which is why the open and interoperable control plane matters more than the current percentages might suggest.”
The next cycle may be cross-vendor collaboration
That same tension — between provider-native convenience and cross-vendor reality — is where Arick Goomanovsky, CEO and cofounder of universal AI agent orchestrator startup BAND, sees the next competitive cycle forming.
“Enterprises now run agents everywhere: individual assistants and coding agents, multi-agent systems in production, agents embedded in Agentforce and ServiceNow, and third-party agents consumed as agent-as-a-service,” Goomanovsky said. “None of them collaborate across those boundaries by default.”
Goomanovsky argues that the missing layer is not just orchestration inside a single model provider, but a cross-vendor collaboration layer that lets agents from different ecosystems act together.
“What’s emerging in parallel is demand for an agentic collaboration harness – an interaction layer that lets agents from Microsoft, OpenAI, Anthropic, and internal teams operate as one workforce,” he said. “Orchestration inside any single vendor is still a walled garden so the next competitive cycle is cross-vendor agent collaboration.”
Independent frameworks face an enterprise packaging problem
There is also a warning sign for independent orchestration frameworks. LangChain and LangGraph fell from 5.4% to 1.4% as the primary orchestration platform in the qualified enterprise sample.
External orchestration abstracted entirely from model providers also fell from 8.9% to 2.9%.
Scott Likens, Global Chief AI Engineer at professional services giant PwC, has a front row seat to this trend as the company spearheads and assists clients with their AI transformations.
As he told VentureBeat in a statement: «Right now, most enterprises are still operating in fragmented environments, with orchestration spread across platforms, business applications, and internally developed tooling. Over time, the market will likely move toward more unified orchestration models, but interoperability, governance and security will remain critical because enterprises are unlikely to standardize on a single agent ecosystem.»
The report argues that fully independent orchestration frameworks may not yet have the enterprise packaging — security certifications, support, compliance documentation and vendor accountability — that procurement teams require.
That does not mean open frameworks are irrelevant. It does suggest that enterprise buyers may increasingly consume open or developer-first orchestration through managed products, cloud-provider partnerships or internal control planes rather than as standalone frameworks.
The agent market starts to look like cloud infrastructure
This is where the agent market starts to look less like the early chatbot market and more like enterprise cloud infrastructure. The winning vendors will not only have capable models. They will have identity integration, permission controls, audit logs, observability, workflow tooling, sandboxing, evaluation and a credible answer to who owns the control plane.
Indeed, the orchestration layer is but one part of the stack that the enterprise must fill in, and enterprises may actually decide to have different orchestration layers for agents working in different departments and functions.
As Nithya Lakshmanan, Chief Product Officer at revenue team AI orchestration startup Outreach.ai wrote in a statement to VentureBeat: «General-purpose orchestration platforms coordinate agent activity well, but they don’t carry the workflow-specific context that determines whether an agent’s action is correct for a given situation. In revenue workflows, an agent acting on incomplete deal history or missing buyer context will underperform and erode trust with users. The teams getting the most out of multi-agent systems are treating domain-specific data as the governance layer, with orchestration sitting on top. Most enterprises have chosen their orchestration stack, and what they’re now figuring out is how those platforms get access to the workflow context they need to make agents useful inside specific business functions.»
That is why Anthropic — which is increasingly launching its own domain-specific agents for finance and design, among other categories — is worth following closely. The company does not need to win the entire orchestration market tomorrow for its strategy to matter. It only needs to persuade a growing set of Claude enterprise customers to let Anthropic handle more of the surrounding machinery: tools, workflows, memory, execution and governance.
If it succeeds, Claude becomes more than a model in a multi-model portfolio. It becomes part of the infrastructure where enterprise work gets done.
That would put Anthropic in a more direct fight with OpenAI and Microsoft — not just over model quality, but over the operating layer of AI agents.
The narrow but important read
The safe interpretation of the VB Pulse data is narrow but important: Anthropic is not yet a major enterprise orchestration platform. Microsoft is. OpenAI is much closer. But Anthropic has registered its first measurable foothold at the orchestration layer, just as the market is deciding who should control agent execution.
For enterprise buyers, that may be the question that matters most in 2026. Not which model is best, but which provider gets to run the agent — and how hard it will be to leave once the agent is running.
New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI agents run.
For the last two years, the enterprise AI race has mostly been framed as a model war: OpenAI’s GPT series versus Anthropic’s Claude versus Google’s Gemini, with smaller and open-source alternatives also coming in from the U.S. and China.
But the next strategic fight may not be over which model answers a prompt best. It may be over who controls the layer where agents plan, call tools, access data, run workflows and prove to security teams that they did not do anything they were not supposed to do.
New VB Pulse survey data suggests the category is already taking shape. Our independent Enterprise Agentic Orchestration tracker, a survey that records the preferences of qualified, verified technical-decision maker respondents at enterprises at regular intervals, found that Microsoft Copilot Studio and Azure AI Studio led with 38.6% primary-platform adoption in February, up from 35.7% in January.
OpenAI’s Assistants and Responses API held second place, rising from 23.2% to 25.7%.
Anthropic remained far smaller, but it made its first appearance in the tracker: moving from 0% in January to 5.7% in February for Anthropic tool use and workflows.
The underlying move is small — four respondents out of a total 70 in this cohort, with more to come — but strategically interesting because it marks the first sign in this tracker of Claude usage moving from the model layer into native orchestration.
That distinction matters. Enterprises are not merely choosing chatbots. They are deciding where the live operational machinery of AI work will sit: inside Microsoft’s stack, inside OpenAI’s API layer, inside Anthropic’s managed runtime, inside an open framework, or across a hybrid mix of all of them.
“This is the convergence moment for enterprise AI,” said Tom Findling, CEO and cofounder of AI cybsersecurity startup Conifers, in a statement to VentureBeat. “Models and agent frameworks have matured enough together that enterprises are now shifting focus beyond model quality to the control plane around it. In security operations, we’re seeing the competitive advantage move toward platforms that can orchestrate agents, leverage enterprise context, and provide governance and auditability across customer environments.”
Anthropic’s number is still small to start — but the increase is not
The Anthropic number, by itself, should not be overread. A move from zero to 5.7% is not a juggernaut. It is not proof that Anthropic has captured enterprise orchestration.
It is not even enough to say Anthropic has a durable lead in any part of this market. Microsoft owns the early enterprise distribution advantage, and OpenAI has a much larger installed base in orchestration than Anthropic.
But small numbers can matter when they appear at the start of a new market structure. Anthropic’s emergence in orchestration comes as the broader VB Pulse data shows Claude also gaining massive enterprise adoption at the model layer.
In our VB Pulse Q1 Foundation Models and Intelligence Platforms tracker, Anthropic rose from 23.9% in January to 28.6% in February and then even more dramatically to 56.2% in March among qualified enterprise respondents, with the March reading flagged as directional only, because the sample was only 16 respondents.
The story, then, is not that Anthropic is winning orchestration today. It is that Anthropic’s model momentum may be starting to spill into the orchestration layer.
That is where the strategic stakes get higher.
A model is easier to swap than an agent runtime
A model is relatively easy to swap, at least in theory. A company can route one workload to Claude, another to GPT, another to Gemini and another to a smaller open model.
In fact, the VB Pulse Foundation Models tracker over the same Q1 period shows that multi-model strategy is the enterprise consensus: respondents increasingly report adopting multiple models and building orchestration layers that route across them by task, cost and risk profile.
An agent runtime is different. Once a company’s workflows, tool permissions, credentials, audit logs, memory, sandboxed execution and operational monitoring live inside one provider’s environment, switching providers becomes less like changing models and more like changing infrastructure.
That is the real reason Anthropic’s 5.7% foothold is worth watching
Anthropic has already made clear that it wants to provide more than the model. Its Claude Managed Agents documentation describes a public beta for a managed agent harness with secure sandboxing, built-in tools and API-run sessions, while Anthropic’s engineering post frames the architecture around decoupling the model from the surrounding agent machinery: the session, the harness and the sandbox.
In plain English, Anthropic is trying to host the environment where Claude agents remember context, use tools, run code, operate inside sandboxes and persist across long-running workflows. That is no longer just inference. That is operational infrastructure.
The pitch is obvious: most enterprises do not want to stitch together their own agent stack from scratch. They want agents that can act, but they also want permission boundaries, audit trails, workflow reliability and ways to stop the system when something goes wrong.
Security is becoming the buying criterion
The VB Pulse orchestration tracker shows that buyers are prioritizing exactly those concerns. Security and permissions ranked as the top orchestration platform selection criterion in both January and February, at 39.3% and 37.1%.
Control over agent execution rose from 17.9% to 22.9%, while flexibility across models and tools fell from 35.7% to 25.7%. The market appears to be shifting from optionality toward governance.
That shift is not surprising. A chatbot can be wrong and still remain mostly contained. An agent that can send emails, modify documents, query databases, call APIs or execute workflows has a much larger blast radius. The enterprise question is not only whether the agent is smart enough.
It is who gave it permission, what it touched, what it changed, whether those actions were logged, and whether the company can unwind the damage if something goes wrong.
Ev Kontsevoy, cofounder and CEO of Teleport, an identity and digital infrastructure solutions company, argues that the industry is still putting too much emphasis on orchestration itself and not enough on identity: “The race to own the agent orchestration layer is real,” Kontsevoy said. “It’s also solving the wrong problem first. Orchestration without identity only multiplies chaos. Without identity, you don’t know what an agent can access, what it actually did, or how to revoke its access when it operates outside policy. A unified identity layer is a prerequisite to deploying agents — one or many — in infrastructure.”
Syam Nair, Chief Product Officer at the enterprise unified data storage solutions firm NetApp, believes data management is key in all cases to secure AI agent orchestration across the enterprise. As he said in a statement to VentureBeat: «Effective agent management requires built-in intelligence and a continuously updated understanding of both data and, critically, its metadata. This visibility allows organizations to define and enforce clear policies so data is used only by the right agents, for the right purposes. Making this work at scale is a crossfunctional effort. Security, storage, and data science teams must work together to implement policies that safeguard company data, while creating a strong data foundation for AI.»
He continued: «The CIOs and technology leaders that are successful are the ones who take the input, policies, and vision from all these teams into account as they build a data infrastructure that minimizes risk and drives business value.»
Microsoft has the distribution edge
That is why Microsoft’s early lead makes sense. Copilot Studio and Azure AI Studio sit inside an enterprise stack many companies already use: Microsoft 365, Teams, Entra ID, Azure and existing procurement relationships.
The VB Pulse Orchestration Tracker for Q1 2026 describes Microsoft as the enterprise default, with no other platform within 13 percentage points in February.
David Weston, CVP, AI Security, Microsoft, provided some insight on why, writing in a statement to VentureBeat: «Without a unified control layer, you start to see fragmentation – agents operating in silos, inconsistent governance, and gaps in security. What customers are asking for is a way to bring order to that complexity. With Agent 365, we’re providing a single control plane to observe, govern, and secure agents across Microsoft, partner, and third-party ecosystems, all grounded in enterprise data and identity.»
OpenAI’s second-place position is also unsurprising. Its Assistants and Responses API gave developers an early way to build agent-like systems using OpenAI’s models and tooling. In the orchestration tracker, OpenAI is not surging, but it is still ticking up steadily: 23.2% in January to 25.7% in February.
Anthropic is the newcomer at the orchestration layer. But its timing may be favorable. The VB Pulse Foundation Models tracker for Q1 2026 suggests enterprises increasingly see Claude as a fit for higher-stakes workloads where safety, instruction following, long context and governance matter.
The orchestration tracker suggests those same buyers are now moving from agent experiments toward production workflows, where security, permissions and task reliability become the gating issues.
That creates a possible path for Anthropic: not to beat Microsoft as the default enterprise platform, at least not immediately, but to become the agent runtime for companies that already trust Claude for sensitive or complex workloads.
The risk is lock-in
The risk for enterprises is lock-in.
The orchestration tracker found that a hybrid control plane — combining provider-native orchestration with external orchestration — was the leading expected architecture, holding around 35% to 36% across the two substantive waves.
Provider-managed-only approaches grew modestly but remained a minority. The report’s conclusion is blunt: enterprises are not willing to give full orchestration control to any single provider.
It makes total sense as enterprises seek to leverage the «best-in-breed» models, harnesses, and tools from multiple vendors, especially as their needs differ widely across sector, business, and size.
«Most enterprises will operate in a multi-model, multi-agent environment, which makes an independent control plane essential,» agreed Felix Van de Maele, CEO of Collibra, a unified data governance startup for AI, in a statement to VentureBeat. «That is why we built AI Command Center: to give organizations the visibility, governance, and real-time oversight needed to manage AI systems and agents across the full lifecycle.»
That caution shows up in the risk data. When asked about risks if agent control lives inside a model provider platform, respondents cited security and permissioning limitations as the top concern. Vendor lock-in was the second-largest concern and the only one that increased from January to February, rising from 23.2% to 25.7%.
This is the tension at the heart of the agent market. Enterprises want managed infrastructure because building reliable agents is hard. But the more a provider manages, the more it may own.
Dr. Rania Khalaf, chief AI officer at WSO2 — the subsidiary of EQT that offers open source, customizable AI stacks for enterprises — said enterprises will need an agent control plane that sits apart from individual frameworks, harnesses and runtimes because agents combine the unpredictability of LLMs with the ability to take actions that have consequences.
“Teams want the freedom to use the best model and framework for each job — Claude for coding, Gemini for writing, LangGraph or CrewAI for dynamic modular behavior — and that heterogeneity makes consistent governance untenable in integrated platforms that lock into one ecosystem,” Khalaf said.
From LLMOps to Agent Ops
Khalaf said the industry is also moving from MLOps to LLMOps to “Agent Ops,” where governance has to cover the whole agent, not just the model call.
“A guardrail on an LLM call can catch hallucination or toxic output, but it will not catch an agent thrashing in an unbreakable, costly loop, which is why governance now has to extend out from the LLM interaction to the scope of the agent,” she said.
The practical implication is that enterprises need to separate policy and control from the agent logic itself. Khalaf pointed to the recent example of an agent deleting a production database despite being told not to, arguing that the failure showed the limits of relying on prompt-level instructions where hard identity and access controls are needed.
“Pulling guardrails, evals, policies, bindings, and agent identity out of the core agent logic allows them to be configured per deployment and per environment, owned by the appropriate teams in security, product, and compliance, without fragmenting the governance layer as different teams choose different models and frameworks,” Khalaf said.
MCP is open. The runtime may still be sticky
That is where Anthropic’s Model Context Protocol, or MCP, complicates the story. MCP is not a walled garden; Anthropic introduced it as an open standard for connecting AI systems to data and tools, and Anthropic’s documentation describes MCP as an open-source standard for connecting AI applications to external systems.
But openness at the protocol layer does not automatically eliminate lock-in at the runtime layer. An enterprise could use an open protocol to connect tools while still becoming dependent on a provider’s managed sessions, logs, sandboxes, permissions model, workflow state and deployment environment. In other words, MCP may reduce integration friction, while managed agent infrastructure could still increase switching costs.
Khalaf said Microsoft’s lead likely reflects its M365 and Azure distribution, while Anthropic’s emerging foothold could reflect a different architectural bet around open protocols such as MCP. But she argued the long-term direction is not a single-provider stack.
“Enterprises serious about running agents in production will end up multi-vendor across these layers,” Khalaf said, “which is why the open and interoperable control plane matters more than the current percentages might suggest.”
The next cycle may be cross-vendor collaboration
That same tension — between provider-native convenience and cross-vendor reality — is where Arick Goomanovsky, CEO and cofounder of universal AI agent orchestrator startup BAND, sees the next competitive cycle forming.
“Enterprises now run agents everywhere: individual assistants and coding agents, multi-agent systems in production, agents embedded in Agentforce and ServiceNow, and third-party agents consumed as agent-as-a-service,” Goomanovsky said. “None of them collaborate across those boundaries by default.”
Goomanovsky argues that the missing layer is not just orchestration inside a single model provider, but a cross-vendor collaboration layer that lets agents from different ecosystems act together.
“What’s emerging in parallel is demand for an agentic collaboration harness – an interaction layer that lets agents from Microsoft, OpenAI, Anthropic, and internal teams operate as one workforce,” he said. “Orchestration inside any single vendor is still a walled garden so the next competitive cycle is cross-vendor agent collaboration.”
Independent frameworks face an enterprise packaging problem
There is also a warning sign for independent orchestration frameworks. LangChain and LangGraph fell from 5.4% to 1.4% as the primary orchestration platform in the qualified enterprise sample.
External orchestration abstracted entirely from model providers also fell from 8.9% to 2.9%.
Scott Likens, Global Chief AI Engineer at professional services giant PwC, has a front row seat to this trend as the company spearheads and assists clients with their AI transformations.
As he told VentureBeat in a statement: «Right now, most enterprises are still operating in fragmented environments, with orchestration spread across platforms, business applications, and internally developed tooling. Over time, the market will likely move toward more unified orchestration models, but interoperability, governance and security will remain critical because enterprises are unlikely to standardize on a single agent ecosystem.»
The report argues that fully independent orchestration frameworks may not yet have the enterprise packaging — security certifications, support, compliance documentation and vendor accountability — that procurement teams require.
That does not mean open frameworks are irrelevant. It does suggest that enterprise buyers may increasingly consume open or developer-first orchestration through managed products, cloud-provider partnerships or internal control planes rather than as standalone frameworks.
The agent market starts to look like cloud infrastructure
This is where the agent market starts to look less like the early chatbot market and more like enterprise cloud infrastructure. The winning vendors will not only have capable models. They will have identity integration, permission controls, audit logs, observability, workflow tooling, sandboxing, evaluation and a credible answer to who owns the control plane.
Indeed, the orchestration layer is but one part of the stack that the enterprise must fill in, and enterprises may actually decide to have different orchestration layers for agents working in different departments and functions.
As Nithya Lakshmanan, Chief Product Officer at revenue team AI orchestration startup Outreach.ai wrote in a statement to VentureBeat: «General-purpose orchestration platforms coordinate agent activity well, but they don’t carry the workflow-specific context that determines whether an agent’s action is correct for a given situation. In revenue workflows, an agent acting on incomplete deal history or missing buyer context will underperform and erode trust with users. The teams getting the most out of multi-agent systems are treating domain-specific data as the governance layer, with orchestration sitting on top. Most enterprises have chosen their orchestration stack, and what they’re now figuring out is how those platforms get access to the workflow context they need to make agents useful inside specific business functions.»
That is why Anthropic — which is increasingly launching its own domain-specific agents for finance and design, among other categories — is worth following closely. The company does not need to win the entire orchestration market tomorrow for its strategy to matter. It only needs to persuade a growing set of Claude enterprise customers to let Anthropic handle more of the surrounding machinery: tools, workflows, memory, execution and governance.
If it succeeds, Claude becomes more than a model in a multi-model portfolio. It becomes part of the infrastructure where enterprise work gets done.
That would put Anthropic in a more direct fight with OpenAI and Microsoft — not just over model quality, but over the operating layer of AI agents.
The narrow but important read
The safe interpretation of the VB Pulse data is narrow but important: Anthropic is not yet a major enterprise orchestration platform. Microsoft is. OpenAI is much closer. But Anthropic has registered its first measurable foothold at the orchestration layer, just as the market is deciding who should control agent execution.
For enterprise buyers, that may be the question that matters most in 2026. Not which model is best, but which provider gets to run the agent — and how hard it will be to leave once the agent is running.
New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI agents run.
For the last two years, the enterprise AI race has mostly been framed as a model war: OpenAI’s GPT series versus Anthropic’s Claude versus Google’s Gemini, with smaller and open-source alternatives also coming in from the U.S. and China.
But the next strategic fight may not be over which model answers a prompt best. It may be over who controls the layer where agents plan, call tools, access data, run workflows and prove to security teams that they did not do anything they were not supposed to do.
New VB Pulse survey data suggests the category is already taking shape. Our independent Enterprise Agentic Orchestration tracker, a survey that records the preferences of qualified, verified technical-decision maker respondents at enterprises at regular intervals, found that Microsoft Copilot Studio and Azure AI Studio led with 38.6% primary-platform adoption in February, up from 35.7% in January.
OpenAI’s Assistants and Responses API held second place, rising from 23.2% to 25.7%.
Anthropic remained far smaller, but it made its first appearance in the tracker: moving from 0% in January to 5.7% in February for Anthropic tool use and workflows.
The underlying move is small — four respondents out of a total 70 in this cohort, with more to come — but strategically interesting because it marks the first sign in this tracker of Claude usage moving from the model layer into native orchestration.
That distinction matters. Enterprises are not merely choosing chatbots. They are deciding where the live operational machinery of AI work will sit: inside Microsoft’s stack, inside OpenAI’s API layer, inside Anthropic’s managed runtime, inside an open framework, or across a hybrid mix of all of them.
“This is the convergence moment for enterprise AI,” said Tom Findling, CEO and cofounder of AI cybsersecurity startup Conifers, in a statement to VentureBeat. “Models and agent frameworks have matured enough together that enterprises are now shifting focus beyond model quality to the control plane around it. In security operations, we’re seeing the competitive advantage move toward platforms that can orchestrate agents, leverage enterprise context, and provide governance and auditability across customer environments.”
Anthropic’s number is still small to start — but the increase is not
The Anthropic number, by itself, should not be overread. A move from zero to 5.7% is not a juggernaut. It is not proof that Anthropic has captured enterprise orchestration.
It is not even enough to say Anthropic has a durable lead in any part of this market. Microsoft owns the early enterprise distribution advantage, and OpenAI has a much larger installed base in orchestration than Anthropic.
But small numbers can matter when they appear at the start of a new market structure. Anthropic’s emergence in orchestration comes as the broader VB Pulse data shows Claude also gaining massive enterprise adoption at the model layer.
In our VB Pulse Q1 Foundation Models and Intelligence Platforms tracker, Anthropic rose from 23.9% in January to 28.6% in February and then even more dramatically to 56.2% in March among qualified enterprise respondents, with the March reading flagged as directional only, because the sample was only 16 respondents.
The story, then, is not that Anthropic is winning orchestration today. It is that Anthropic’s model momentum may be starting to spill into the orchestration layer.
That is where the strategic stakes get higher.
A model is easier to swap than an agent runtime
A model is relatively easy to swap, at least in theory. A company can route one workload to Claude, another to GPT, another to Gemini and another to a smaller open model.
In fact, the VB Pulse Foundation Models tracker over the same Q1 period shows that multi-model strategy is the enterprise consensus: respondents increasingly report adopting multiple models and building orchestration layers that route across them by task, cost and risk profile.
An agent runtime is different. Once a company’s workflows, tool permissions, credentials, audit logs, memory, sandboxed execution and operational monitoring live inside one provider’s environment, switching providers becomes less like changing models and more like changing infrastructure.
That is the real reason Anthropic’s 5.7% foothold is worth watching
Anthropic has already made clear that it wants to provide more than the model. Its Claude Managed Agents documentation describes a public beta for a managed agent harness with secure sandboxing, built-in tools and API-run sessions, while Anthropic’s engineering post frames the architecture around decoupling the model from the surrounding agent machinery: the session, the harness and the sandbox.
In plain English, Anthropic is trying to host the environment where Claude agents remember context, use tools, run code, operate inside sandboxes and persist across long-running workflows. That is no longer just inference. That is operational infrastructure.
The pitch is obvious: most enterprises do not want to stitch together their own agent stack from scratch. They want agents that can act, but they also want permission boundaries, audit trails, workflow reliability and ways to stop the system when something goes wrong.
Security is becoming the buying criterion
The VB Pulse orchestration tracker shows that buyers are prioritizing exactly those concerns. Security and permissions ranked as the top orchestration platform selection criterion in both January and February, at 39.3% and 37.1%.
Control over agent execution rose from 17.9% to 22.9%, while flexibility across models and tools fell from 35.7% to 25.7%. The market appears to be shifting from optionality toward governance.
That shift is not surprising. A chatbot can be wrong and still remain mostly contained. An agent that can send emails, modify documents, query databases, call APIs or execute workflows has a much larger blast radius. The enterprise question is not only whether the agent is smart enough.
It is who gave it permission, what it touched, what it changed, whether those actions were logged, and whether the company can unwind the damage if something goes wrong.
Ev Kontsevoy, cofounder and CEO of Teleport, an identity and digital infrastructure solutions company, argues that the industry is still putting too much emphasis on orchestration itself and not enough on identity: “The race to own the agent orchestration layer is real,” Kontsevoy said. “It’s also solving the wrong problem first. Orchestration without identity only multiplies chaos. Without identity, you don’t know what an agent can access, what it actually did, or how to revoke its access when it operates outside policy. A unified identity layer is a prerequisite to deploying agents — one or many — in infrastructure.”
Syam Nair, Chief Product Officer at the enterprise unified data storage solutions firm NetApp, believes data management is key in all cases to secure AI agent orchestration across the enterprise. As he said in a statement to VentureBeat: «Effective agent management requires built-in intelligence and a continuously updated understanding of both data and, critically, its metadata. This visibility allows organizations to define and enforce clear policies so data is used only by the right agents, for the right purposes. Making this work at scale is a crossfunctional effort. Security, storage, and data science teams must work together to implement policies that safeguard company data, while creating a strong data foundation for AI.»
He continued: «The CIOs and technology leaders that are successful are the ones who take the input, policies, and vision from all these teams into account as they build a data infrastructure that minimizes risk and drives business value.»
Microsoft has the distribution edge
That is why Microsoft’s early lead makes sense. Copilot Studio and Azure AI Studio sit inside an enterprise stack many companies already use: Microsoft 365, Teams, Entra ID, Azure and existing procurement relationships.
The VB Pulse Orchestration Tracker for Q1 2026 describes Microsoft as the enterprise default, with no other platform within 13 percentage points in February.
David Weston, CVP, AI Security, Microsoft, provided some insight on why, writing in a statement to VentureBeat: «Without a unified control layer, you start to see fragmentation – agents operating in silos, inconsistent governance, and gaps in security. What customers are asking for is a way to bring order to that complexity. With Agent 365, we’re providing a single control plane to observe, govern, and secure agents across Microsoft, partner, and third-party ecosystems, all grounded in enterprise data and identity.»
OpenAI’s second-place position is also unsurprising. Its Assistants and Responses API gave developers an early way to build agent-like systems using OpenAI’s models and tooling. In the orchestration tracker, OpenAI is not surging, but it is still ticking up steadily: 23.2% in January to 25.7% in February.
Anthropic is the newcomer at the orchestration layer. But its timing may be favorable. The VB Pulse Foundation Models tracker for Q1 2026 suggests enterprises increasingly see Claude as a fit for higher-stakes workloads where safety, instruction following, long context and governance matter.
The orchestration tracker suggests those same buyers are now moving from agent experiments toward production workflows, where security, permissions and task reliability become the gating issues.
That creates a possible path for Anthropic: not to beat Microsoft as the default enterprise platform, at least not immediately, but to become the agent runtime for companies that already trust Claude for sensitive or complex workloads.
The risk is lock-in
The risk for enterprises is lock-in.
The orchestration tracker found that a hybrid control plane — combining provider-native orchestration with external orchestration — was the leading expected architecture, holding around 35% to 36% across the two substantive waves.
Provider-managed-only approaches grew modestly but remained a minority. The report’s conclusion is blunt: enterprises are not willing to give full orchestration control to any single provider.
It makes total sense as enterprises seek to leverage the «best-in-breed» models, harnesses, and tools from multiple vendors, especially as their needs differ widely across sector, business, and size.
«Most enterprises will operate in a multi-model, multi-agent environment, which makes an independent control plane essential,» agreed Felix Van de Maele, CEO of Collibra, a unified data governance startup for AI, in a statement to VentureBeat. «That is why we built AI Command Center: to give organizations the visibility, governance, and real-time oversight needed to manage AI systems and agents across the full lifecycle.»
That caution shows up in the risk data. When asked about risks if agent control lives inside a model provider platform, respondents cited security and permissioning limitations as the top concern. Vendor lock-in was the second-largest concern and the only one that increased from January to February, rising from 23.2% to 25.7%.
This is the tension at the heart of the agent market. Enterprises want managed infrastructure because building reliable agents is hard. But the more a provider manages, the more it may own.
Dr. Rania Khalaf, chief AI officer at WSO2 — the subsidiary of EQT that offers open source, customizable AI stacks for enterprises — said enterprises will need an agent control plane that sits apart from individual frameworks, harnesses and runtimes because agents combine the unpredictability of LLMs with the ability to take actions that have consequences.
“Teams want the freedom to use the best model and framework for each job — Claude for coding, Gemini for writing, LangGraph or CrewAI for dynamic modular behavior — and that heterogeneity makes consistent governance untenable in integrated platforms that lock into one ecosystem,” Khalaf said.
From LLMOps to Agent Ops
Khalaf said the industry is also moving from MLOps to LLMOps to “Agent Ops,” where governance has to cover the whole agent, not just the model call.
“A guardrail on an LLM call can catch hallucination or toxic output, but it will not catch an agent thrashing in an unbreakable, costly loop, which is why governance now has to extend out from the LLM interaction to the scope of the agent,” she said.
The practical implication is that enterprises need to separate policy and control from the agent logic itself. Khalaf pointed to the recent example of an agent deleting a production database despite being told not to, arguing that the failure showed the limits of relying on prompt-level instructions where hard identity and access controls are needed.
“Pulling guardrails, evals, policies, bindings, and agent identity out of the core agent logic allows them to be configured per deployment and per environment, owned by the appropriate teams in security, product, and compliance, without fragmenting the governance layer as different teams choose different models and frameworks,” Khalaf said.
MCP is open. The runtime may still be sticky
That is where Anthropic’s Model Context Protocol, or MCP, complicates the story. MCP is not a walled garden; Anthropic introduced it as an open standard for connecting AI systems to data and tools, and Anthropic’s documentation describes MCP as an open-source standard for connecting AI applications to external systems.
But openness at the protocol layer does not automatically eliminate lock-in at the runtime layer. An enterprise could use an open protocol to connect tools while still becoming dependent on a provider’s managed sessions, logs, sandboxes, permissions model, workflow state and deployment environment. In other words, MCP may reduce integration friction, while managed agent infrastructure could still increase switching costs.
Khalaf said Microsoft’s lead likely reflects its M365 and Azure distribution, while Anthropic’s emerging foothold could reflect a different architectural bet around open protocols such as MCP. But she argued the long-term direction is not a single-provider stack.
“Enterprises serious about running agents in production will end up multi-vendor across these layers,” Khalaf said, “which is why the open and interoperable control plane matters more than the current percentages might suggest.”
The next cycle may be cross-vendor collaboration
That same tension — between provider-native convenience and cross-vendor reality — is where Arick Goomanovsky, CEO and cofounder of universal AI agent orchestrator startup BAND, sees the next competitive cycle forming.
“Enterprises now run agents everywhere: individual assistants and coding agents, multi-agent systems in production, agents embedded in Agentforce and ServiceNow, and third-party agents consumed as agent-as-a-service,” Goomanovsky said. “None of them collaborate across those boundaries by default.”
Goomanovsky argues that the missing layer is not just orchestration inside a single model provider, but a cross-vendor collaboration layer that lets agents from different ecosystems act together.
“What’s emerging in parallel is demand for an agentic collaboration harness – an interaction layer that lets agents from Microsoft, OpenAI, Anthropic, and internal teams operate as one workforce,” he said. “Orchestration inside any single vendor is still a walled garden so the next competitive cycle is cross-vendor agent collaboration.”
Independent frameworks face an enterprise packaging problem
There is also a warning sign for independent orchestration frameworks. LangChain and LangGraph fell from 5.4% to 1.4% as the primary orchestration platform in the qualified enterprise sample.
External orchestration abstracted entirely from model providers also fell from 8.9% to 2.9%.
Scott Likens, Global Chief AI Engineer at professional services giant PwC, has a front row seat to this trend as the company spearheads and assists clients with their AI transformations.
As he told VentureBeat in a statement: «Right now, most enterprises are still operating in fragmented environments, with orchestration spread across platforms, business applications, and internally developed tooling. Over time, the market will likely move toward more unified orchestration models, but interoperability, governance and security will remain critical because enterprises are unlikely to standardize on a single agent ecosystem.»
The report argues that fully independent orchestration frameworks may not yet have the enterprise packaging — security certifications, support, compliance documentation and vendor accountability — that procurement teams require.
That does not mean open frameworks are irrelevant. It does suggest that enterprise buyers may increasingly consume open or developer-first orchestration through managed products, cloud-provider partnerships or internal control planes rather than as standalone frameworks.
The agent market starts to look like cloud infrastructure
This is where the agent market starts to look less like the early chatbot market and more like enterprise cloud infrastructure. The winning vendors will not only have capable models. They will have identity integration, permission controls, audit logs, observability, workflow tooling, sandboxing, evaluation and a credible answer to who owns the control plane.
Indeed, the orchestration layer is but one part of the stack that the enterprise must fill in, and enterprises may actually decide to have different orchestration layers for agents working in different departments and functions.
As Nithya Lakshmanan, Chief Product Officer at revenue team AI orchestration startup Outreach.ai wrote in a statement to VentureBeat: «General-purpose orchestration platforms coordinate agent activity well, but they don’t carry the workflow-specific context that determines whether an agent’s action is correct for a given situation. In revenue workflows, an agent acting on incomplete deal history or missing buyer context will underperform and erode trust with users. The teams getting the most out of multi-agent systems are treating domain-specific data as the governance layer, with orchestration sitting on top. Most enterprises have chosen their orchestration stack, and what they’re now figuring out is how those platforms get access to the workflow context they need to make agents useful inside specific business functions.»
That is why Anthropic — which is increasingly launching its own domain-specific agents for finance and design, among other categories — is worth following closely. The company does not need to win the entire orchestration market tomorrow for its strategy to matter. It only needs to persuade a growing set of Claude enterprise customers to let Anthropic handle more of the surrounding machinery: tools, workflows, memory, execution and governance.
If it succeeds, Claude becomes more than a model in a multi-model portfolio. It becomes part of the infrastructure where enterprise work gets done.
That would put Anthropic in a more direct fight with OpenAI and Microsoft — not just over model quality, but over the operating layer of AI agents.
The narrow but important read
The safe interpretation of the VB Pulse data is narrow but important: Anthropic is not yet a major enterprise orchestration platform. Microsoft is. OpenAI is much closer. But Anthropic has registered its first measurable foothold at the orchestration layer, just as the market is deciding who should control agent execution.
For enterprise buyers, that may be the question that matters most in 2026. Not which model is best, but which provider gets to run the agent — and how hard it will be to leave once the agent is running.
