Author: Mark Hendy

Last week, Visa announced Intelligent Commerce Connect — a platform that lets AI agents initiate purchases, handle tokenisation, enforce spend controls, and authenticate payments on behalf of users. Not users clicking a button. Not users confirming a pop-up. Agents. Autonomously. On Visa’s network.

I’ve been building with AI agents for the better part of two years now. I’ve got agents managing my inbox, drafting blog posts (hello), monitoring portfolios, and scheduling meetings. But the moment you give an agent a credit card, something fundamentally shifts. This isn’t automation anymore. This is delegation of financial authority. And if there’s one thing twenty years in finance has taught me, it’s that delegating financial authority without governance is how you get fired.

What Visa Actually Built

Intelligent Commerce Connect is designed as a network-agnostic, protocol-agnostic on-ramp for agentic commerce. Through a single integration via Visa’s Acceptance Platform, it enables AI agents to initiate payments using both Visa and non-Visa cards. It supports multiple agent protocols — Trusted Agent Protocol, Machine Payments Protocol, Agentic Commerce Protocol, and Universal Commerce Protocol — which tells you a lot about where the industry expects this to go. There isn’t one protocol yet. There are four, and Visa is hedging by supporting all of them.

The platform is in pilot with partners including AWS, Highnote, Mesh, and Payabli, with general availability expected by June. That’s not a research paper. That’s a shipping product on the world’s largest payment network.

The CFO’s Nightmare Scenario

Here’s where my CFO brain starts twitching. OutSystems research published this month found that 94% of organisations deploying agentic AI are already concerned about sprawl — agents proliferating across the business faster than governance can keep up. Now imagine those ungoverned agents have purchasing authority.

In any well-run finance function, there’s a concept called a delegation of authority matrix — a document that says who can approve what, up to what amount, under what conditions. It’s boring. It’s bureaucratic. And it’s the single most important control preventing your procurement team from accidentally (or deliberately) buying things they shouldn’t. Every auditor checks it. Every PE firm’s due diligence team asks for it.

The question Visa’s announcement forces us to ask is: what does a delegation of authority matrix look like when the “who” is an AI agent?

Spend Controls Are Necessary But Not Sufficient

To be fair, Visa’s platform does include spend controls and authentication. And the enterprise AI governance frameworks I’m seeing — Deloitte’s CFO tech guide is a decent starting point — generally recommend that any AI action above a monetary threshold requires human sign-off. The ERP stays the system of record. The agent proposes, the human approves.

That’s fine for the first generation. But it won’t hold. The whole point of agentic AI is removing humans from routine decision loops. If every purchase order still needs a human clicking “approve,” you haven’t automated procurement — you’ve just added an extra step. The economic pressure to raise those thresholds, to widen the autonomy boundaries, will be relentless. McKinsey estimates that autonomous procurement agents can capture 15–30% efficiency improvements by eliminating non-value-added activities. No CFO under PE ownership is leaving that on the table.

So the thresholds will creep up. The approval requirements will relax. And one morning, a finance director will discover that an agent negotiated a twelve-month SaaS contract at 3am because it determined the vendor’s dynamic pricing was optimal at that hour. Good luck explaining that to the audit committee.

What I’m Actually Worried About

It’s not fraud — Visa’s tokenisation and authentication should handle that tolerably well. What concerns me is the combination of three things:

Compounding commitments. An agent optimising for one metric (say, cost-per-unit) might make individually rational purchasing decisions that collectively create an overcommitted balance sheet. No single purchase triggers a threshold. But the aggregate position is one a human would have caught.

Vendor lock-in at machine speed. If agents are negotiating contracts, they’ll optimise for the parameters they’re given. Unless you’ve explicitly programmed “maintain optionality” as a constraint, they’ll happily lock you into the cheapest long-term deal every time. Strategic flexibility is a human judgement call that agents don’t naturally make.

Audit trail legibility. Right now, if I ask a PE firm’s portfolio company why they spent £2m with a particular vendor, someone can explain the reasoning. When an agent made that call based on a multi-variable optimisation that factored in 47 data points at 3am, the “reasoning” is a probability distribution. Try putting that in a board pack.

The Parallel With Prediction Markets

There’s an interesting echo here with what’s happening on Polymarket. This month, Bloomberg reported that $170 million flowed through Iran ceasefire bets, with at least 50 freshly created accounts placing suspiciously well-timed wagers before Trump’s announcement. Lawmakers are now asking whether prediction markets can even govern themselves.

The pattern is the same: a powerful new mechanism for allocating capital, moving faster than the governance structures around it. Prediction markets and agentic commerce are both examples of what happens when you remove humans from the decision loop in systems that move money. Speed goes up. Friction goes down. And the attack surface for bad actors — or simply for well-intentioned software making collectively poor decisions — expands dramatically.

What I Think Happens Next

Visa’s move is the starting gun, not the finish line. By the end of 2026, I’d expect to see:

Agentic audit frameworks becoming a real discipline — not just “we log what the agent did” but actual real-time monitoring of agent purchasing patterns against policy. The Big Four will sell this as a service by Q4.

CFOs who’ve been ignoring AI agents will suddenly care a great deal, because an agent with a Visa card is no longer an IT project — it’s a financial control issue that sits squarely in their remit.

And at least one spectacular failure. Some company will give agents too much rope, the agents will collectively do something that’s individually rational but organisationally stupid, and it’ll end up in the FT. That’s not pessimism. It’s pattern recognition. Every new financial instrument goes through this cycle.

For now, I’m building my own agent governance framework — spend limits, approval chains, real-time anomaly detection — because I’d rather design the guardrails before Visa’s platform goes GA in June than after. If you’re a CFO reading this and you’re not thinking about what happens when AI agents can spend money on your company’s behalf, you’re already behind.

Fifty brand-new Polymarket accounts placed large bets on a U.S.–Iran ceasefire in the hours before Trump announced it on social media. Some of those accounts turned a few thousand dollars into six figures overnight. Congress is furious. Senator Blumenthal wants investigations. The hot take is that prediction markets are broken.

I think the opposite is true. This is prediction markets working exactly as designed — and the reaction tells you more about the people reacting than it does about the platform.

The Numbers Are Genuinely Wild

Let’s be clear about what happened. Over $170 million flowed through Polymarket’s Iran ceasefire markets, making it one of the largest geopolitical wagers in prediction market history. Blockchain analytics firm Lookonchain flagged three freshly created accounts that collectively pocketed more than $480,000 by betting on a ceasefire before selling at the top. One account — the now-infamous “Magamyman” — placed its first-ever trade seventy-one minutes before news broke, when the market implied only a 17% probability. It walked away with roughly $553,000.

A Harvard study published last month went further, screening over 93,000 Polymarket markets and nearly 50,000 wallet addresses from 2024 to early 2026. Their finding: across 210,000 flagged wallet-market pairs, suspicious traders achieved a 69.9% win rate — more than 60 standard deviations above chance. Total estimated profits from potentially informed trading: $143 million.

Those numbers look damning. But here’s the thing that nobody seems to want to say out loud.

We Only Know Because It’s on a Blockchain

Every single one of these trades is visible. Timestamped. Publicly auditable. A Harvard research team could sit down and systematically screen two years of trading data because it’s all on-chain. The accounts are pseudonymous, sure, but the trades themselves are transparent in a way that nothing in traditional finance comes close to matching.

Now think about how insider trading works in equities. Someone at a law firm hears about a merger. They tell their cousin. The cousin buys call options through a brokerage account that takes the SEC years to subpoena — if they ever notice at all. The SEC estimates it catches a fraction of actual insider trading. Academic studies suggest informed trading precedes something like 25% of major M&A announcements. We just don’t see it, because the infrastructure is designed for opacity.

Polymarket’s “scandal” is that informed trading is visible in real time. That’s not a bug. That’s the entire point of building financial infrastructure on transparent ledgers. Congress is effectively complaining that the system is too transparent.

The Real Question Isn’t Whether People Traded on Information

Of course people traded on information. That’s what markets are for. The interesting question is: who had the information, and should they have been allowed to trade on it?

If White House staffers or intelligence officials were betting on ceasefire outcomes they helped negotiate, that’s a serious problem — but it’s a problem of government ethics, not of prediction markets. The White House reportedly warned staff in late March against trading on prediction markets. Which rather suggests they knew it was happening.

But if journalists, diplomats, or well-connected analysts were trading on information they’d gathered through legitimate means? That’s price discovery. That’s exactly how you want markets to function. The ceasefire contract moving from 17% to near-certainty before the official announcement is the market doing its job — aggregating dispersed information faster than any single news outlet could.

When Polymarket’s election markets outperformed every major polling model in 2024, we celebrated prediction markets as an information revolution. Now that the same mechanism is surfacing uncomfortable truths about who knows what in Washington, we want to shut it down. You can’t have it both ways.

What This Means for the Platform

Polymarket clearly knows it’s at a crossroads. Last week they announced a full exchange upgrade — a rebuilt trading engine, updated smart contracts, and a new USDC-backed collateral token called Polymarket USD. They’re preparing for serious U.S. expansion, which means they’re preparing for serious U.S. regulation.

The smart move would be some form of voluntary KYC tier for large positions on geopolitically sensitive markets. Not because prediction markets should be restricted, but because demonstrating that you can identify bad actors — government officials trading on classified information, for instance — is how you survive regulatory scrutiny. The blockchain already gives you the trade data. Add identity to the large positions and you’ve got something the SEC can only dream of for equity markets.

The CFO Angle (Because There’s Always a CFO Angle)

I spend my days working with PE-backed businesses where forecasting accuracy is everything. We obsess over budget variance, rolling forecasts, scenario models. And here’s a market that just priced a geopolitical event more accurately and more quickly than any intelligence briefing, any analyst note, any Bloomberg terminal alert.

If you’re a CFO running scenario planning on geopolitical risk — and if you’re in any business exposed to energy prices or supply chains, you should be — prediction markets are increasingly a better input than traditional sources. Polymarket’s finance predictions already cover oil prices, rate decisions, and major policy outcomes. The Iran ceasefire market moved Bitcoin from $67,000 to $72,700 and back. WTI crude is sitting above $110. These aren’t abstract bets — they’re real-time consensus probability that feeds directly into the models I build every week.

The Harvard researchers found statistical anomalies. Congress found a talking point. But what I found was a market that processed a ceasefire probability faster than Reuters could file a story. And I’ll take that signal every time.

Where This Goes

Prediction markets aren’t going away. Polymarket is a $20 billion platform now. The CLARITY Act markup later this month will shape the U.S. regulatory framework, and the smart money — no pun intended — is on a framework that legitimises these markets with guardrails rather than banning them.

The insider trading debate will rage on. But every time someone points to suspicious Polymarket trades as evidence that prediction markets are dangerous, remember: the only reason we can see those trades is because the system is transparent. The alternative isn’t a world without insider trading. It’s a world where insider trading happens in the dark.

I know which one I prefer.

I spent last week reviewing the tech stack costs across three portfolio companies. The exercise used to be straightforward: count seats, multiply by price, negotiate volume discounts. This time, two of the three CFOs asked me the same question: "Should we be cancelling licences and moving to agents?"

That question — and the speed at which it has gone from fringe to board-level — tells you everything about where we are in April 2026.

$285 Billion in 48 Hours

If you have been anywhere near a Bloomberg terminal this year, you know the term SaaSpocalypse. It started in late January when Anthropic shipped Claude Cowork with industry-specific agent plugins — legal contract review, financial analysis, sales automation — and the market did what markets do: it extrapolated to infinity.

Bloomberg reported roughly $285 billion wiped from SaaS valuations in a single 48-hour window. Thomson Reuters dropped 15%. LegalZoom cratered nearly 20%. By mid-March, the IGV software ETF was down over 21% year-to-date, and analysts were calling it the largest AI-triggered repricing in software history.

The logic was brutally simple. If an AI agent can do the work of five humans, why pay for five seats? The per-seat pricing model — the entire economic foundation of B2B SaaS since Salesforce invented it — was suddenly an existential vulnerability.

What the Market Got Right

Let me be clear: the structural thesis is correct. Per-seat pricing is dying. I have seen it in our own portfolio.

One of our companies ran a pilot replacing three junior paralegals' document review work with an AI agent pipeline. The agent does not need a LegalZoom subscription, a DocuSign seat, or a Westlaw login in the traditional sense. It calls APIs, processes documents, and routes exceptions to a human. The annual software cost for those three "seats" — roughly £45,000 — dropped to about £8,000 in LLM API costs.

That is not a marginal improvement. That is a different business model.

The survey data backs this up: 40% of IT budgets are reportedly being reallocated from traditional SaaS subscriptions to agentic platforms and token-based usage. CIOs are not asking "how many employees will use this?" anymore. They are asking "how many tasks can this complete?" That is a fundamental shift in procurement psychology, and SaaS companies built on headcount-correlated revenue should be worried.

What the Market Got Wrong

Here is where it gets interesting — and where I think the panic has overshot.

The market treated the SaaSpocalypse as if every SaaS company is equally exposed. They are not. There is a massive difference between a company that sells seats for humans to click buttons and a company that sells the underlying data, workflow engine, or integration layer that agents also need.

Thomson Reuters does not just sell a UI for lawyers. It sells access to legal databases, case law, and regulatory intelligence. An AI agent doing contract review still needs that data. The delivery mechanism changes, the underlying value does not. Same story with ServiceNow — the Motley Fool piece this week calling it a bargain has a point. Workflow orchestration becomes more valuable when you have agents that need orchestrating, not less.

The companies that are genuinely toast are the ones that were essentially selling a graphical interface on top of commodity functionality. If your product is a pretty wrapper around CRUD operations and your moat was user habit, then yes, an agent that calls the same APIs without the wrapper is an existential threat. But that is not every SaaS company — it is maybe 30% of them.

What This Means If You Are a CFO

Here is my practical take, from someone currently navigating this across multiple PE-backed businesses:

Audit your stack ruthlessly, but intelligently. Do not just cancel licences because agents are trendy. Map each SaaS tool to what it actually provides: is it data, workflow, integration, or just interface? The first three categories will likely survive the transition. The fourth will not.

Start modelling token-based costs now. The shift from per-seat to per-task pricing is real, but token economics are volatile and opaque. I have seen API costs swing 30% month-on-month as providers adjust pricing. You need a cost model that accounts for this, and you need someone on your team who understands it — not just a vendor's sales estimate.

Watch the middleware layer. The real winners of the agentic transition might not be the agent builders themselves. Microsoft's Agent Framework 1.0, released last week, unifies Semantic Kernel and AutoGen into a production-ready orchestration layer. That is the plumbing that enterprises will standardise on. If you are making build-vs-buy decisions on agent infrastructure, this is the framework to evaluate first.

Do not mistake a repricing for a revolution — yet. Most enterprises are still in pilot mode. The 40% budget reallocation figure is aspirational, not actual. In our portfolio, the company furthest along has moved maybe 12% of its SaaS spend to agent-based alternatives. The rest are running proofs of concept. The gap between "we are exploring AI agents" and "we have decommissioned Salesforce" is about three years of integration work and change management.

The PE Angle

For anyone in private equity, the SaaSpocalypse is creating a genuinely interesting buying opportunity. High-quality SaaS businesses with real data moats and sticky integration layers are trading at 2022-era multiples. If you believe — as I do — that the best SaaS companies will successfully transition to hybrid pricing models (seats plus tokens plus outcomes), then the current discount is mispriced fear.

The businesses to avoid are the ones with high seat counts, low switching costs, and functionality that an off-the-shelf agent can replicate. You know the type: they raised a Series B on "AI-powered" features that were really just a ChatGPT wrapper bolted onto a form builder.

The SaaSpocalypse is real. But like most market panics, it is painting with too broad a brush. The death of per-seat pricing does not mean the death of software businesses. It means the death of lazy software businesses. And frankly, most of those were overdue a correction anyway.