Block today unveiled Managerbot, a new AI agent embedded in the Square platform that proactively monitors a seller’s business, identifies emerging problems, and proposes actionable solutions — without the seller ever having to ask a question. The product marks the most tangible manifestation of CEO Jack Dorsey’s controversial bet that artificial intelligence can fundamentally reshape how his company operates, builds products, and serves the millions of small businesses that depend on Square to run day-to-day commerce.
In an exclusive interview with VentureBeat, Willem Avé, Block’s head of product at Square, described Managerbot as a decisive break from the company’s earlier Square AI assistant, which functioned as a reactive chatbot that answered seller questions about sales, employees, and business performance.
«The big shift from Square AI to Managerbot is really from reactive to proactive,» Avé said. «What that means is the primary interface is not a question box. You assign tasks to Managerbot, and that could be based on data, an insight, or a signal from your business.»
The product is beginning to roll out now, with full availability to Square sellers expected over the coming months. Block declined to say whether Managerbot would carry an additional fee or be bundled into existing Square subscriptions.
How Managerbot predicts inventory shortages, optimizes schedules, and writes marketing campaigns on its own
Avé outlined three core domains where Managerbot operates today: inventory forecasting, employee shift scheduling, and automated marketing campaign creation. In every case, the agent acts before the seller does — watching over the business, detecting patterns, and surfacing recommendations with proposed actions attached.
In the inventory domain, Managerbot continuously monitors a seller’s stock levels, sales velocity, and external signals such as weather patterns and local events, then alerts the seller when an item is about to run out — or when it should stock up ahead of anticipated demand. «In warmer weather, we can see that you sell more of a certain good,» Avé explained. «That’s the forecasting capability, combined with local data — weather, events — so we can help sellers manage both their inventory and cash flows.»
For shift scheduling — a task that Avé described as «one of those interesting, very hard computer science problems» that consumes hours of a small business owner’s week — Managerbot analyzes forecasted sales data and then generates optimized employee schedules that balance worker preferences with coverage needs. «It turns out that frontier models are actually pretty good at it,» Avé said.
The third capability tackles what Avé called «the whole bucket of things that sellers could do if they had more time» — principally marketing. Managerbot identifies sales trends across a seller’s catalog and automatically drafts win-back campaigns and promotional outreach targeted at a store’s best customer segments. Avé said Block is seeing «very meaningful lift» from Managerbot-generated campaigns compared to what some sellers create manually, though he declined to share specific performance figures publicly.
Block built Managerbot on frontier AI models from OpenAI and Anthropic — but says the real innovation is underneath
Managerbot runs on third-party frontier models — Avé specifically referenced Anthropic’s Sonnet and OpenAI’s GPT family — but Block’s competitive advantage, he argued, lies in the «agent harness» the company has built around those models. That harness draws heavily on Goose, Block’s open-source agent framework, and incorporates learnings from its consumer-facing Money Bot on Cash App.
The challenge specific to Square is scale and complexity. A seller running a small business might interact with hundreds of different tools across invoicing, inventory, customer management, marketing, payroll, and scheduling. Managerbot must navigate all of them coherently within a single agentic loop. «This isn’t like, you know, you load a skill and call it a day — think about hundreds of skills,» Avé said. «Actually, managing the context and managing the way that we progressively disclose tools, and some of the other innovation that we have at the harness layer, is I think some of the secret sauce.»
A critical design decision shapes every interaction: Managerbot does not autonomously execute changes to a seller’s business. Every write action — whether adjusting a shift schedule, publishing a marketing campaign, or modifying inventory — requires explicit seller approval. To facilitate that approval, Managerbot generates visual UI previews showing exactly what will change before the seller clicks «yes.» «We want to earn trust with sellers, so any write action is prompted to the user to approve,» Avé said. «The seller needs a visual representation of what the change is. You can’t just describe in words all the time what you’re going to go do.»
An $80 million fine and chatbot blunders hang over Block’s push to automate financial recommendations
That human-in-the-loop caution reflects a sensitivity that gains additional weight given Block’s recent history. In January 2025, 48 state financial regulators imposed an $80 million fine on Block for violations of Bank Secrecy Act and anti-money laundering laws related to Cash App. The Connecticut Department of Banking stated in announcing the settlement that regulators «found Block was not in compliance with certain requirements, creating the potential that its services could be used to support money laundering, terrorism financing, or other illegal activities.» The Illinois Department of Financial and Professional Regulation simultaneously joined the coordinated enforcement action.
Separately, reporting from The Guardian has documented instances of Block’s customer-facing chatbots making serious errors, including telling customers to cancel or close their accounts. When VentureBeat raised this concern during the interview, Avé acknowledged the stakes but redirected to Managerbot’s specific safeguards.
«Financial accuracy and financial data — the value of these products really come from recommendations,» Avé said. «We need to be better than whatever you can feed to ChatGPT. If you take a CSV of your sales and put it in ChatGPT or Claude, we need our product to be better and answer that question either more accurately or better than what’s available in the market.» He pointed to the harness layer’s role in reducing hallucinations through tuning, prompt engineering, and optimized tool-call loops, while acknowledging the inherent limitations of probabilistic systems: «It’s never going to be zero. Obviously, these are probabilistic systems, and we have guidance and call-outs in the tool to provide that.» On regulated domains like lending and payments, Avé was more definitive: «In any sort of regulated domains — banking, lending, payments — there are strict guardrails on what we can and can’t say to sellers. Those are just part of the product and business.»
Dorsey cut 4,000 jobs in the name of AI — Managerbot is the first answer to what those tools are actually building
It is impossible to evaluate Managerbot outside the context of the radical organizational surgery Block performed just weeks ago. In late February, Dorsey announced that Block would cut more than 4,000 of its roughly 10,000 employees — nearly half the workforce — explicitly citing AI as the driving rationale. As the BBC reported, Dorsey wrote that «AI fundamentally changes what it means to build and run a company.» Block’s stock surged more than 20 percent on the news, according to ABC7.
The company’s Q4 2025 earnings report, released alongside the layoff announcement, showed gross profit of $2.87 billion — up 24 percent year over year — and raised 2026 guidance to $12.2 billion in gross profit, according to AlphaSense’s earnings analysis. Block also reported a greater than 40 percent increase in production code shipped per engineer since September 2025 through the use of agentic coding tools. As CNBC commentator Steve Sedgwick wrote in an opinion piece following the announcement, «I keep getting told on CNBC that AI will create new jobs to replace those being lost. I’ve been asking the same question for years now.» The Observer’s Mark Minevich was more pointed, calling Block’s layoffs «probably the first legitimate mass layoff driven by A.I. as the actual operating thesis.»
Managerbot, then, is the product answer to the obvious follow-up question: if Block shed 4,000 workers in the name of intelligence tools, what exactly are those intelligence tools building? Avé framed the product as proof of concept for Block’s entire strategic thesis. «Block has been in the press recently about rebuilding as an intelligence company, and it’s like, a lot of people are asking, ‘What does that mean for us?'» Avé said. «What I like to do is show, not tell. We’re building Managerbot, which I think is one of the more advanced, maybe the most advanced, small business agent out there today.»
Sellers who use Managerbot are consolidating their businesses onto Square — and that may be the real strategic payoff
Perhaps the most consequential signal Avé shared was an early behavioral pattern: sellers who begin using Managerbot are voluntarily migrating more of their business operations onto the Square platform, consolidating payroll, time cards, and shift scheduling into Block’s ecosystem to feed the agent more data. «When they start interacting with Managerbot, they want to move more of their business onto Square because they see the value,» Avé said. «They’re like, ‘I should put my payroll here. I should get time cards here. I should get my shift schedules here,’ because once all that data is in one place, they can make better decisions and manage their business better.»
This dynamic could prove to be Managerbot’s most significant long-term effect — not as a standalone feature, but as a gravitational force pulling sellers deeper into Block’s integrated commerce stack. Block’s Q4 earnings already showed Square’s new volume added grew 29 percent year over year, with sales-led NVA surging 62 percent. Avé also argued that Square’s first-party architecture — built organically rather than through acquisitions — gives it a structural advantage over competitors in the AI era. «We’ve kind of harmonized and canonicalized this data at a sensible layer,» he said. «It’s not super hard to create more skills for these data domains.»
When VentureBeat pressed Avé on the tension between helping sellers and upselling them on Block’s own financial products — lending, payments processing, and other services that generate revenue for the company — he acknowledged the concern but framed Managerbot’s mission in terms of decision-making quality. «The goal for Managerbot is to help sellers increase their decision-making correctness,» Avé said. «If we can make sellers better at running their business by making better decisions and giving time back, I think that’s a good thing.»
Block says Managerbot isn’t a chatbot — it’s a business protector that compounds the company’s entire AI strategy
Avé was insistent that Managerbot represents something categorically different from the chatbot-as-advisor model that has proliferated across enterprise software. «A lot of people are building chatbots as advisors — it can answer a question for you,» he said. «What we really want Managerbot to be is a protector of your business. This is identifying trends. This is spotting things that you might have missed. This is helping you run your business and take actions.»
He also argued that the agent model compounds Block’s development velocity in ways that traditional software cannot match. «It’s much more straightforward to add a capability to Managerbot than it is to build a big Web 2.0 UI,» Avé said. «If we can deliver more capabilities, more features, more value to our sellers, the whole system compounds.»
Whether that compounding materializes — and whether sellers ultimately experience Managerbot as a trusted protector or a sophisticated upsell engine — will determine much about Block’s future. The company has staked its corporate identity, its headcount, and its Wall Street narrative on the conviction that AI agents can deliver more value with fewer humans in the loop. Managerbot is the first product to carry the full weight of that promise. And the small business owners who keep their shops open with Square terminals, who juggle shift schedules on napkins and skip marketing because there aren’t enough hours in the day — they didn’t ask to be the test case for Silicon Valley’s boldest AI thesis. But as of today, they are.
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