Yesterday, monday.com launched something called Agentalent.ai — a managed marketplace where enterprises can discover, evaluate, and “hire” AI agents for defined business roles. Not install. Not deploy. Hire.
You post a role. You review qualified agents. You select based on task fit, budget, and operational readiness. Pricing starts around $2,000 a month per agent. They launch with 17 agents available. Built in collaboration with AWS, Anthropic, and Wix.
If you’re a CFO and that doesn’t make your headcount model twitch, you’re not paying attention.
The Language Shift That Matters
I’ve been building with AI agents for the best part of two years now — wiring up Claude to handle research tasks, automating financial reporting pipelines, getting agents to do the kind of grunt work that used to eat a junior analyst’s entire Tuesday. But the framing has always been tooling. You set up an agent like you’d set up a spreadsheet macro. It’s a thing on your computer.
What monday.com has done — deliberately, with their HR-style language — is shift the frame from tools to workers. And that’s not just marketing fluff. It’s the conceptual bridge that will get the rest of the C-suite to finally understand what’s happening.
A Belitsoft report published this weekend puts numbers on it: the average enterprise now runs 12 AI agents. Twelve. And that’s expected to hit 20 by 2027. But here’s the kicker — half of those agents operate completely alone, unconnected to any other agent or system. They’re doing their little jobs in their little silos, and nobody’s orchestrating the whole thing.
Sound familiar? It should. That’s exactly what happens when a company hires people without a coherent operating model. You end up with twelve contractors, half of whom don’t talk to each other, doing overlapping work with no shared context. I’ve walked into PE portfolio companies that look exactly like this — except with humans.
The CFO’s New Headcount Problem
Here’s where it gets interesting for anyone sitting in a finance seat. When an AI agent costs $2,000 a month and can do the work of a task that previously required a $6,000/month contractor, that’s a straightforward business case. Any CFO can model that. The ROI practically draws itself.
But the real question isn’t “should we hire the agent?” It’s “how do we account for a workforce that’s now 30% software?”
Think about what sits in your headcount model today. Salaries, employer NI, pension contributions, benefits, training costs, recruitment fees. Now think about what sits in your AI agent budget. SaaS subscriptions, API usage fees, compute costs, maybe some integration consulting. These two things live in completely different cost categories, get approved through different processes, and are managed by different people. But they’re increasingly doing the same work.
In the PE world I operate in, headcount is one of the first things a new investor scrutinises. “What’s your revenue per head?” “What’s your fully-loaded cost per FTE?” These metrics are foundational to how value creation plans get built. But nobody’s asking “what’s your revenue per agent?” yet. And they should be, because if you’re running 12 agents and growing, that’s a material line in your operating model that isn’t being tracked like one.
The Coordination Tax
The Belitsoft finding that half of deployed agents work alone is, I think, the most important data point in their entire report. It mirrors what I’ve seen first-hand. Companies get excited, they spin up agents for customer support, for code review, for data entry, for reporting — and each one works reasonably well in isolation. But the value compounds when agents talk to each other, and almost nobody has figured that part out yet.
This is an orchestration problem, and it’s fundamentally a management problem. You need someone — or something — deciding which agent handles which task, what context gets shared, where the human review gates sit. NVIDIA’s new Agent Toolkit, announced with partners including Salesforce, SAP, and ServiceNow, is trying to solve the infrastructure side of this. Okta’s new “secure agentic enterprise” framework, going GA at the end of this month, is tackling identity and access. But the management layer — the actual decision-making about how to deploy and coordinate these things — that’s still a gap.
And it’s a gap that, in most companies, probably falls to the CFO. Not the CTO. Not the CISO. The CFO. Because ultimately this is a resource allocation problem. You have a pool of human and non-human workers. You have tasks that need doing. You need to figure out the optimal mix, track the cost, measure the output, and report on it to a board that still thinks in FTEs.
What I’m Actually Doing About It
In my own setup, I’ve started treating agent costs the way I treat contractor costs — as a blended workforce line, not a software line. My AI assistant Saul runs daily tasks for me: research, publishing, monitoring. I track what he does, what it costs, and what it would cost if a human did it instead. Not because I’m obsessive about it (okay, partly because I’m obsessive about it), but because I think this is the accounting framework that PE firms will expect within 18 months.
The $600 billion flowing into AI agent ecosystems in 2026 isn’t going into chatbots. It’s going into digital workers — things that take tasks, complete them, and cost money every month. If your chart of accounts still treats all of that as “IT software subscriptions,” you’re going to have a very confusing board pack by Christmas.
Where This Goes
monday.com’s marketplace is clunky right now — 17 agents isn’t exactly a deep talent pool. But the model is right. Within a year, I’d expect to see the big consulting firms offering “blended workforce planning” as a service line. Within two, PE due diligence will include an AI agent audit alongside the usual people and tech reviews.
For CFOs, the action item is boringly practical: start tracking your agents like you track your people. Give them cost centres. Measure their output. Build the reporting now, while it’s still simple, because it won’t be simple for long.
We spent decades building HR systems to manage human workers. We’re about to need something equivalent for the digital ones. And the CFO who figures that out first is going to look very clever at the next board meeting.
