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The number of active agents in the Microsoft 365 ecosystem grew 15 times in a year, and 18 times in large enterprises. That’s from Microsoft’s 2026 Work Trend Index. Deloitte cites a Gartner forecast that the average Fortune 500 company “is likely to have 150,000 artificial intelligence agents by 2028.”
Neither number says who’s watching any one of those agents, or what happens the first time one gets something wrong.
You’ll get three questions once an agent goes near your people’s data. Your CEO asks what agentic AI in HR could take on. Your CIO asks what it can reach. Your employees ask who sees what, and who makes the call. Here’s an answer to the last one for 122 real HR agents, counted.
What counts as agentic AI in HR: three traits
Three traits, and you can check any product against them.
An HR agent reads real HR data: the HRIS, the applicant tracking system, payroll, calendars, the policy library. It takes more than one step toward a specific HR job, so it might compare two systems, rank what it found, and draft a note to the person who owns the problem.
And it starts on a trigger. Something happens, a schedule comes round, or somebody asks. Many agents on our map start from something that just happened: a candidate moves to an interview stage, payroll inputs close, a hiring manager picks someone to offer.
A chatbot answers the question in front of it. An agent works across steps toward a job, and that’s why HR automation built on agents raises the who-decides question so quickly.
Our answer to the who-decides question is a live, counted map of 122 HR agents, across 12 HR areas, with a line on every one saying who decides.
Every agent on our map is built around a person, on purpose
We don’t build systems that make autonomous decisions about people. AI may inform, surface, rank, summarize or draft. A person makes every call about a person, and the system is built so it can’t do otherwise.
On the map, that rule shows up twice on every agent’s page. The first is a written line saying who decides. The second is a diagram of the data flow, and the diagram puts the person in a specific spot.
Where a person approves or reviews, the diagram draws them on the path from the agent to everything it produces, labelled DECIDES or REVIEWS. Nothing reaches the outputs without passing through them. Where a person sets the rules, the diagram draws them above the agent as a control point, with an arrow into it and a note that they can override any output. For the one agent with no people decision, it draws no person at all, and says so.
Microsoft’s Work Trend Index gets to a version of the same question. It says there are “three questions that every Frontier Firm will need to answer: Who reviews agent performance? Who has the authority to update the workflows that agents run? How does a local win get captured and scaled across the organization?”
The first two are about where a person sits. That’s the line we write on every agent’s page.
The four ways a person sits in the flow
Every one of the 122 agents carries one of four values for where the person sits. Here’s the count, with one example for each, and the who-decides line quoted word for word from that agent’s page.
| How a person sits in the flow | Agents | What happens | Example | Who decides, from the agent’s page |
|---|---|---|---|---|
| Approves: on the path, deciding | 66 | The agent drafts or proposes, and nothing goes out until a person signs off | Offer Drafting Agent | ”The hiring manager, compensation and HR approve the numbers and terms; the agent drafts and flags and does not set pay.” |
| Reviews: on the path, checking | 25 | Same spot on the path, and the person checks the agent’s work against what they’d expect | Payroll Anomaly Detection Agent | ”The payroll manager reviews every flag and decides what is an error, what is legitimate, and what to correct before sign-off; the agent surfaces and explains, it does not change any pay line.” |
| Sets rules: off the path, controlling | 30 | A person sets the rules up front, the agent runs inside them without a sign-off on each run, and any output can be overridden | Interview Scheduling Agent | ”The recruiter sets who interviews and in what order; the agent proposes and books slots and any human can override.” |
| None: no people decision | 1 | The job isn’t a decision about anyone, so the map draws no gate | Total Rewards Statement Agent | ”No people decision is made here; total rewards owns the valuation methodology and approves the statement template, and the employee sees only their own data.” |
Add the first two rows together and 91 of the 122 agents never produce an output without a person touching it first.
Approves. The Offer Drafting Agent starts when a hiring manager selects a candidate to offer. It assembles the offer letter and approval packet from the approved template, level, location and pay band. It flags three things: pay outside the band, internal-equity gaps against peers in the same role, and clauses that need a check for that jurisdiction. Then it stops.
Its guardrails say it never sets or suggests a pay number, and nothing is sent without the approval chain.
Reviews. The Payroll Anomaly Detection Agent runs when payroll inputs close before each pay run. It reconciles the HRIS, time and absence data and the payroll file, then ranks what looks wrong: pay swings against the last cycle, leavers still on the file, negative or zero net pay. The payroll manager works through the list. The agent can’t edit a pay line, the payroll file or the HRIS.
This gate has a weak spot, and the map names it. The first pitfall on the agent’s page reads: “Too many low-value flags and the payroll team stops reading.” A review only protects anyone while the reviewer is still reading, so the proof for this agent passes only if “the false-flag list stays short enough that the team keeps reading it.”
Sets rules. The Interview Scheduling Agent starts when a candidate moves to an interview stage. It works through panel calendars, the candidate’s time zone, rooms and video links, and sends the invites and prep material. It handles reschedules without a recruiter stepping in.
None. The Total Rewards Statement Agent builds each employee a personal statement covering base pay, variable pay, equity, benefits, pension contributions and paid time off, and explains each line in plain language. Nobody’s pay changes because of it. It reads only the employee’s own record.
Why approves and reviews are split, and what sets rules allows
Approves and reviews share one diagram shape. The person sits on the path and nothing ships without them. Only the label on the gate changes, from DECIDES to REVIEWS. So why split them?
Because the person is doing different work. With an approves agent, the draft arrives and the person makes the decision it serves: the pay, the terms, the wording that goes to a candidate. With a reviews agent, the person already owns a process, and the agent has run a check on it. The payroll manager reads each flag and judges whether it’s an error or a legitimate swing, like a bonus month or back pay.
Twelve agents on the map do jobs where the work itself is a decision about a person: screening applications, pay options for one person, a promotion case, a PIP, a restructure. The map’s verdict for those twelve is human decides. All twelve carry approves. None of them reviews, sets rules or runs with no gate.
The second question: if a person only sets the rules, is the agent acting on its own?
Every output stays inside rules a person set, and any human can override any output. For Interview Scheduling, the recruiter sets the panel, the order and the hours, and can cancel any slot. The log records who overrode what. Its first guardrail reads: “Books only within the panel and order the recruiter set.”
What the growth numbers don’t tell you
Go back to the numbers at the top. Fifteen-fold growth in a year, and a forecast of 150,000 agents in the average Fortune 500 company by 2028. Both count agents. Who decides on each of them is a separate count, and neither source gives it.
Deloitte’s research on how AI agents fit into the human workplace puts two findings next to each other. In a Deloitte study of senior managers and above, two-thirds said disruptions from multi-agent AI could significantly transform how work is performed within the next two to three years. Only about a quarter said their business processes or workforce are prepared for that transformation.
The same piece reports that nearly 70% of respondents to a recent Deloitte survey named “an inability to trust and govern agents” as a major barrier to realizing value from agentic technology.
Here’s our reading, which Deloitte doesn’t state itself. Agents are arriving faster than the answer to who decides on each one. And on the employee side, trust tracks with use. Deloitte’s TrustID research finds that workers with high levels of trust in their organizations are 3.8 times as likely to use agentic technology.
If you’ve got a shortlist of agents, from a vendor demo or your own team’s ideas, give each one a line: who approves, who reviews, who sets the rules, or that no people decision is made. An agent you can’t write that line for is one to hold.
The 10X HR-AI Framework scores one idea in eight questions, and who decides is part of the score. The strategy month does it for your whole list. Each idea is scored and sequenced, and each one gets a named decision-owner, drawn the way the map draws one for each of its 122. The month is a fixed fee. Each use case after it is a fixed fee for an agreed scope, quoted after the proof, and hosting and model usage are billed by your own providers.
Book a 30-minute call and bring the shortlist.
Frequently asked questions
What is an AI agent in HR?
An AI agent in HR reads real HR data, takes more than one step toward a specific HR job, and starts on a trigger: an event, a schedule, or someone asking. A payroll agent that reconciles three systems before each pay run and ranks what looks wrong is one.
Who decides when an HR AI agent gets something wrong?
That depends on where the person sits, and on our map it’s written down for every agent. In 66 of the 122, a person approves before anything goes out. In 25, a person reviews the agent’s work. In 30, a person sets the rules up front and can override any output. One agent makes no people decision at all.
Can an AI agent in HR make a hiring or firing decision on its own?
Not on our map, and not in anything we build. We don’t build systems that make autonomous decisions about people. Twelve agents on the map do jobs that are themselves decisions about a person, and all 12 put a person on the path to approve. The agent drafts, checks and organizes, and the person makes the call.
Is an AI agent the same as an HR chatbot?
No. A chatbot answers one question, and an agent works across several steps toward a job, which is why an agent needs a line saying who decides.
How many AI agents does HR actually need?
The map has 122 across 12 HR areas, and no company needs all of them at once. The order they come in is the decision that matters, because the early ones earn the trust the later ones depend on. Working out that order for your list is what the strategy month does.