As enterprises confront AI agent sprawl, xpander wants them to own their own control and context layer

Enterprise AI has a new infrastructure problem: companies are accumulating agents faster than they are developing systems to govern them.

Gartner estimates that the average global Fortune 500 company will have more than 150,000 AI agents in use by 2028, up from fewer than 15 in 2025. Yet only 13% of organizations believe they currently have the right AI agent governance in place, according to the research firm.

That widening gap is creating a market for infrastructure that sits above individual models and agents — handling execution, permissions, observability, memory, access to enterprise systems and lifecycle management without forcing developers to reconstruct those services for every new agent.

xpander.ai, a startup founded by three former AWS principal engineers, is the latest company trying to own that layer.

The company is making its enterprise AI agent platform generally available today, positioning it as a vendor-neutral control plane for building, running and governing agents across different models, agent frameworks and infrastructure environments.

In an exclusive interview with VentureBeat, xpander.ai CEO and co-founder David Twizer said the company increasingly hears three problems from enterprise customers: agents running locally without centralized governance, agent workflows remaining isolated to individual users, and infrastructure becoming tied to a single AI provider.

“The third issue is the most critical part: it’s being locked into one vendor,” Twizer told VentureBeat. “Everything that you do is actually owned by the company that you chose to work with — their tools, their roadmap, their political view of how agents should react to everything that you do.”

The caveat is that xpander’s vendor neutrality does not eliminate dependency; it moves the dependency up the stack. Enterprises can swap models, frameworks and infrastructure underneath xpander, but xpander’s proprietary Universal Harness and control plane become the layer coordinating execution, governance, identity, memory and auditability.

That could create a new form of lock-in if those configurations and operational state are difficult to migrate to another control plane. xpander’s public documentation does not yet explain how portable that layer is if a customer terminates its enterprise license.

The company is also announcing a $7.5 million seed round led by Pico Venture Partners, with participation from Emerge Ventures, Samsung Next and SeedIL.

But xpander is entering a market that has evolved considerably beyond a simple divide between proprietary hyperscaler platforms and neutral alternatives.

The battle is shifting above the model

Model portability is becoming increasingly common in agent infrastructure.

LangChain’s LangSmith Deployment, for example, provides infrastructure for deploying and governing production agents. Enterprises can run its control plane and agent servers entirely inside their own Kubernetes infrastructure, including for air-gapped and data-residency-sensitive environments. LangChain also offers hybrid deployments in which the customer operates agent servers while LangChain manages the control plane.

CrewAI is moving in much the same direction. Its enterprise offering includes centralized governance, SSO, role-based access controls, workload identity and policies, while supporting deployment in CrewAI’s cloud, a customer’s VPC or customer-owned infrastructure. CrewAI says its platform can work across models and clouds and lets customers retain and modify the code generated for their agents.

Temporal approaches the problem from another direction. Rather than offering an end-to-end agent management environment, it provides durable execution for long-running AI workflows, including crash recovery, retries, human approvals and state that can persist through failures. Its AI platform can orchestrate workflows across different models, tools and systems.

Meanwhile, the model and cloud vendors themselves are expanding upward into this infrastructure layer. OpenAI’s Frontier platform provides shared enterprise context, permissions, agent execution and management, while Google’s Gemini Enterprise Agent Platform combines managed runtime infrastructure with agent identity, centralized governance, memory, tracing and policy enforcement around agent-to-agent and agent-to-tool interactions.

In other words, xpander’s differentiator cannot simply be that enterprises can run agents outside one model provider’s cloud. Several competitors already offer versions of that flexibility.

Its bigger bet is that enterprises will want a framework-independent control plane sitting above an increasingly heterogeneous collection of models, agent frameworks and employee-facing AI interfaces.

Twizer’s argument draws partly on his seven years at AWS, where he watched enterprises wrestle with a previous generation of infrastructure lock-in.

“Our strategy was to bring the great thing called cloud computing and then try to create solutions that are serverless and, by definition, create vendor lock-in,” Twizer told VentureBeat. But, he said, enterprises ultimately demanded the ability to move workloads among providers.

“I think AI is no different,” he said. “Multi-cloud, multi-vendor is a must-have strategy.”

A runtime intended to survive model churn

At the center of xpander’s platform is what it calls a Universal Harness — a model-, framework- and cloud-agnostic runtime for executing agents as portable enterprise workloads.

Companies can use xpander’s hosted environment or, under its enterprise offering, self-deploy on Kubernetes or on-premises infrastructure. The company also explicitly lists AWS, Google Cloud, Microsoft Azure, private VPCs and fully air-gapped on-premises environments as supported deployment targets.

The framework claim is similarly broad: xpander says customers can bring agents built with frameworks including LangChain, Strands and Agno, as well as their existing prompts, rules and skills. It supports proprietary, open-weight and customer fine-tuned models.

For developers, xpander exposes three main integration routes: a language-agnostic REST API for control-plane operations, a Python SDK for building agents and workflows, and Model Context Protocol support that exposes agents and tools to MCP clients such as Claude Desktop and Cursor. The REST interface includes operations for managing and invoking agents, tasks and knowledge bases, while the SDK adds event handling and agent lifecycle abstractions.

“Think like a Kubernetes environment that runs inside your own premises, your private cloud or our xpander cloud,” Twizer told VentureBeat. “The harness that is agnostic can run any agent of any vendor.”

Twizer said the goal is eventually to make model selection more analogous to choosing compute underneath an application than committing an organization to an entire software ecosystem.

“You can go from Claude to ChatGPT to Kimi and to your own fine-tuned models,” he said. “All of them will be used in the same way that you use CPUs. You don’t really care who makes them. You just want your application running.”

That architecture is intended to let the underlying workloads change while the orchestration and operational layer remains relatively stable.

The distinction could become more important as enterprises use more than one AI supplier. OpenAI itself says enterprise AI is shifting from assistance toward delegated work performed by agents, with its latest enterprise data showing rapidly growing agentic usage across functions including legal, sales, recruiting and marketing.

At the same time, Google is adding centralized agent governance, identity and gateway controls specifically to manage interactions between agents and tools at scale. Those investments from much larger vendors reinforce xpander’s basic premise: managing the model is increasingly only one part of putting agents into production.

What ‘governance’ means in practice

Twizer argues that locally running agents make that operational problem particularly acute.

“You install Claude or ChatGPT or Codex or any other AI system that runs on the employee laptop, and you get a black box,” he told VentureBeat. “Nothing is governed, nothing is audited, and nothing is controlled.”

His concern is not simply that an agent can access local resources, but that AI products have rapidly moved from generating text to taking actions without enterprises necessarily building equivalent governance infrastructure around them.

“It happened overnight that the shift from, ‘Here is the chatbot that helps me write an email,’ became, ‘It is an AI agent that can control my machine and perform API calls,’” Twizer said.

xpander’s documentation provides a more concrete definition of what the company means by moving that activity into a governed environment.

The control plane can define who is allowed to run an agent, which resources the agent can reach and which actions require human approval. xpander says agents receive named identities and actions can be traced back to the human who invoked them. Tool calls, runs, traces, approvals and failures are logged, with spending attributable at the task level.

One particularly relevant architectural detail for security teams is credential handling. xpander says credentials are injected from a vault when a tool call executes, rather than being exposed directly to the model.

The company says it is SOC 2 Type II certified and GDPR compliant. Its enterprise tier also includes SSO and OIDC, a private model gateway and sub-organizations with per-team usage attribution.

Where xpander is trying to separate itself from products such as LangSmith and CrewAI is in treating the underlying agent framework itself as another replaceable component rather than making its own framework the primary development environment.

The company says its runtime can operate xpander-native agents alongside custom agents built with other frameworks. That broader framework neutrality is a company claim and will need to prove itself in production; the overlap among these platforms is already substantial.

The cost of building the agent layer yourself

For enterprises that want centralized control without adopting another vendor’s infrastructure, the alternative is increasingly to assemble many of these components themselves.

Twizer argues that this is where the hidden cost of production agents emerges. Accessing a foundation model is comparatively straightforward; recreating the operational infrastructure surrounding a sophisticated managed agent is not.

“If you want to do something like the amazing [Anthropic] Claude Code in a way that is governed and secure, you need to build the entire harness,” Twizer told VentureBeat. “You need to build the sandbox and the authentication and the human in the loop and the storage and the session management and the memory layer.”

“It’s like investing two years of infrastructure just to run that agent in a secure way,” he added.

The company’s documentation says its harness handles long-horizon and multi-tool tasks, sandboxed code execution, persistent memory and recovery when tasks fail or drift. That is important context for Twizer’s argument: xpander isn’t merely offering a gateway that swaps one model API for another; it is attempting to package many of the runtime services enterprises otherwise have to operate alongside those APIs.

Twizer said xpander has heard from government organizations and financial institutions using its technology in production that this infrastructure burden was preventing them from deploying agents in a controlled way.

The company’s website separately displays logos for organizations including Lenovo, Intel, Workday, Siemens, Nvidia, Intuit, SAP, PagerDuty, Mozilla and Salesforce under the label “Used by teams at.”

The company’s answer is not that enterprises should abandon products from Anthropic, OpenAI or other AI providers. Twizer instead argues that companies should retain those models while moving permissions, monitoring and execution into an infrastructure layer they control.

“It’s not, ‘Don’t use Claude,’” he said. “It’s actually, ‘Use Claude, but in a way that you watch it, that you secure it, that you monitor it,’” with controls over authorized skills and who can access particular resources.

Making agents organizational rather than personal

xpander is also adding a collaboration layer it calls Multiplayer AI.

The premise is that enterprise agents increasingly need to outlive a single user’s chat session. A workflow might involve an agent collecting data, another system taking an action, an employee approving it and a different team resuming the process hours or days later.

Twizer argues that locally deployed assistants have another disadvantage: the expertise employees accumulate through their agents — prompts, skills, workflows and context — tends to stay with those individuals.

“You’re making the AI agents better, and you’re not making the company better,” he told VentureBeat. “All the data, all the prompts, all the workflows, all the skills that you are now investing in that ecosystem are being locked inside that vendor, and it’s very hard to share it with other colleagues.”

More complex enterprise work also rarely maps neatly onto a single chat session.

“Work is not single player. It’s multiplayer,” Twizer said. “It’s happening over the period of multiple days, even weeks. Workflows that are significant to enterprises are not chatbots.”

xpander’s shared conversations are designed to keep those interactions in persistent, permission-scoped threads. An agent can be published once for use across an organization, while employees can interact with agents through Slack, Teams, ChatGPT, Claude and xpander’s own interface.

The company’s documentation says downstream actions can identify as the human who invoked the agent through end-to-end authentication using OIDC — a potentially important distinction for organizations that need to preserve user-level identity and authorization as work moves through an agent.

The requirement for persistent execution is not unique to xpander. Temporal has made long-running, failure-resistant workflows the core of its AI pitch, while LangSmith supports persistent agent deployment and production observability.

What xpander is attempting to combine is that runtime infrastructure with enterprise collaboration and governance into a single, portable, flexible, vendor-neutral control plane.

Omni tests the harness thesis

The company is also making Omni, its prebuilt agent, generally available.

Calling Omni simply a general-purpose agent understates what xpander is now selling. Its documentation describes Omni as an AI forward-deployed engineer that turns a requested business outcome into what the company calls an “Agentic Application”: a backend agent coupled with a frontend experience that can include chat, interactive UI components, reports, dashboards and visualizations.

A user can describe the desired outcome, after which Omni is designed to help construct the application, connect its model, skills, tools and data, execute work as tracked tasks, and maintain files and reusable context in an Agent Workspace. The resulting application can then be shared with teammates.

For example, xpander’s documentation shows a user asking Omni to build a Redshift Analyzer. Omni is intended to gather the requirements, design the interface, construct the backend agent, attach appropriate connectors and tools, and generate the first live application surface.

The documentation also lists Slack, WhatsApp, Telegram and email as Omni channels, while xpander’s broader platform materials list Teams, Claude, ChatGPT, API, webhook, CLI and MCP among the ways work can reach agents. Because the company’s materials describe slightly different channel sets for Omni and the broader platform, enterprises should verify support for their particular interface and deployment configuration.
Omni can also function alongside desktop AI products rather than replacing them. Twizer said employees can continue using their preferred desktop agents while Omni acts as a skill exposing only enterprise-approved data and connectors. When background execution is needed, Omni can create a centrally controlled agent to perform the work.

xpander says Omni scored 90.9% on the GAIA benchmark, including strong performance on its most difficult tasks. The result is company-reported and should be viewed as evidence for xpander’s broader thesis rather than independent validation of the platform.

Twizer said the company deliberately tested the harness with a mixture of models rather than optimizing around a single proprietary system.

“We took Opus, we took Sonnet, we took Kimi, we took GPT, and on that benchmark we got a 90.9% score,” he told VentureBeat.

For Twizer, the result supports an argument that agent performance increasingly depends on the infrastructure surrounding the foundation model.

“It just proves that the value is in the harness layer and not in the model layer,” he said. “The value is in: How do you handle long-running tasks? How do you handle tool calling? How do you do sandboxing? How do you focus? How do you make the agents focused on the task?”

The benchmark results are publicly available, allowing others to examine the company’s methodology, but the broader conclusion remains xpander’s interpretation of its own results.

That thesis is increasingly shared across the industry. The disagreement is over who should control that layer.

OpenAI and Google are turning it into part of broader AI platforms. LangChain and CrewAI are extending their development ecosystems into enterprise runtimes. Temporal is attacking the execution problem as infrastructure.

xpander is betting enterprises will instead want that control plane to remain independent of all three choices: model, cloud and agent framework.

The company now has to demonstrate that the extra abstraction is valuable enough to justify another platform in an enterprise AI stack that is already becoming crowded.

Pricing and Availability

xpander’s platform and Omni are generally available now, with two substantially different commercial models for teams using xpander’s hosted service and enterprises running the platform on their own infrastructure.

The self-service Team offering has no seat charge and is priced in credits based on agent activity. xpander defines one credit as one cent, meaning $100 buys 10,000 credits. Each event or message that wakes an agent costs one credit for the entire turn, regardless of how long the turn runs, and each tool or API call costs another credit. Model tokens are billed separately in credits at configured rates for the selected model.

That makes the cost structure more granular than Twizer’s description of paying for agent “output” might suggest. Long-running execution itself does not appear to create a duration-based charge under the published pricing, but a workflow can accumulate costs through repeated invocations, tool calls and model-token consumption.

New accounts receive 1,000 free credits rather than a time-limited free trial, and customers can sign up with a credit card. The Team tier permits unlimited agents, workflows and seats, according to xpander.

Model usage can represent a significant portion of total cost, and the company publishes examples intended to make that expense calculable. Its pricing page lists different input and output token rates for individual models and says those model charges are incorporated into the credit system, eliminating a separate model-provider bill for hosted customers. Actual spending therefore depends heavily on the models selected, token volumes and number of tool calls.

Enterprises that self-host get a different arrangement. xpander’s Enterprise plan is an annual license starting at 50 agents and supports deployment on a customer’s Kubernetes infrastructure or on-premises environment. It includes SSO and OIDC, sub-organizations with pooled credits and per-team usage reporting, private model-gateway connectivity, onboarding and Tier 1 support. Customers can also bring their own model API keys and use their own negotiated model-provider rates.

xpander does not publish the dollar price of that annual enterprise license, so prospective customers cannot calculate the total cost of a private or air-gapped deployment from public information alone. The pricing materials also do not provide a public dollar figure for the 50-agent starting commitment. Those deployments require contacting xpander for custom pricing.

“You pay only for the work that the agent is doing,” Twizer told VentureBeat of the hosted model. “No subscription fee, no seat fee, no builder seat, nothing. You just pay for the actual output of the agents.”

For enterprise buyers, that distinction is important: xpander’s no-seat-fee claim applies most straightforwardly to its usage-based hosted offering. Organizations that need to own the deployment environment are instead evaluating an undisclosed annual license, infrastructure costs and their own model-provider spend.

That leaves xpander with a straightforward but increasingly contested pitch as enterprises confront agent sprawl: keep using whichever models, frameworks and interfaces employees find useful, but move the infrastructure controlling what those agents can do into a common layer the organization can govern.

Whether enterprises ultimately want that layer from an independent startup, an agent framework company or the same hyperscalers and model providers xpander wants to abstract away is now becoming another battleground in the enterprise AI stack.

How xpander compares with other agent infrastructure

The overlap becomes clearer when the products are compared directly. Several rivals now support multiple model providers, customer-controlled infrastructure or both. The differences increasingly concern how much of the agent stack each platform attempts to own — and whether it provides an agent development framework, a neutral runtime, durable workflow infrastructure or a collection of managed cloud services.

The comparison complicates a simple vendor-neutrality pitch. AWS AgentCore, for example, supports models outside Bedrock and frameworks including LangGraph, CrewAI and Google’s ADK, while LangSmith explicitly calls itself framework agnostic. CrewAI offers customer-controlled deployments and broad model choice. Temporal lets companies self-host the durable execution layer entirely, although it solves a narrower infrastructure problem rather than offering a complete agent control plane.

That makes xpander’s potential differentiation more specific. It is trying to combine framework and model portability with enterprise identity, governance, runtime infrastructure and an employee-facing collaboration layer, rather than competing only on the ability to swap models or deploy outside a vendor’s SaaS environment. xpander also prices its hosted platform around individual agent wakes and tool calls rather than seats, although enterprises requiring self-hosting face an undisclosed annual license beginning at 50 agents.