The operating model for agentic AI
Don't prompt.
Design.
A one-page canvas and an AI coach for the nine decisions that make an agent useful, governable and worth building, before code turns assumptions into operating reality.
Agentic AI design is org design: settle the job, the authority, the knowledge and the accountability before the worker shows up.
Developed by Michael Freeman, INSEAD
The diagnosis
The model keeps improving.
The failure rate does not.
The cause is not in the technology. An un-designed agent is not un-designed; it is designed by accident, in code, by whoever shipped first. The briefing nobody wrote. The boundaries nobody drew. The oversight nobody owns. A person you hire restrains themselves, remembers, and can be held to account; an agent brings none of it. Every design decision you skip to close that gap is a debt, and model capability is the interest rate.
new hire
Worker the LLM
Capable, articulate, probabilistic. It arrives trained on the world and briefed on none of your work.
Harness the operating system around it
The part you control. Five things, each already familiar from managing people:
Tools the systems it can act on
A web search, a database query, an email sent. The worker issues a command, the harness catches it, the system executes. An LLM in a loop, with tools.
The Agent Operating Model
Every agent has an anatomy.
Each of the three layers supplies one term of the same equation. Most failures trace to the same blind spot. The worker is chosen with care, then wrapped in a harness and a governance regime that nobody designed.
power = cognition × control × reach
It is a product, not a sum. A brilliant worker with wide reach and no control isn't powerful; it is a liability. Add a calendar and the worker becomes a scheduler. Give it the authority to move money and it becomes a fiduciary actor. The more reach you grant, the more control the harness has to hold.
The canvas
Nine decisions, before the build.
One North Star. Four design questions. Nine operating decisions. The Agentic AI Design Canvas turns the Agent Operating Model into choices a team makes on paper, before anyone writes a line of code.
Agentic AI Design Canvas
North Star
What business outcome must this agent ultimately achieve?
Target Workflow & Success Metrics
Which workflow will it improve, and which metrics will show it worked?
01Users & Stakeholders
Who uses it directly, and who is affected by what it does?
02Performance Needs
What accuracy, explainability, speed, cost, and other standards must it meet?
03Context & Knowledge
What information is available to support its work, and which sources should it trust (e.g. policies, cases, records, reports, communications)?
07Memory & Learning
What should it remember, what must it forget, and how will it improve over time?
08Ownership & Oversight
Who owns the outcome, who oversees the agent, and when must a person intervene?
09
- Assist
- helps a person do the work.
- Advise
- recommends a course of action.
- Prepare
- assembles a ready-to-use output for approval.
- Decide
- selects the outcome, but does not carry it out.
- Execute
- carries out the authorised action or workflow.
Choose the highest rung the agent reaches anywhere in the scoped workflow.
Select a cell for its question, or a category for its territory
What business outcome must this agent ultimately achieve?
One outcome the whole design serves. Every cell below answers to it.
Job what it's for
01Target Workflow & Success Metrics
Which workflow will it improve, and which metrics will show it worked?
An agent pointed at a cost-only metric will optimise it relentlessly, at scale, even when the workflow plainly implies a quality outcome.
02Users & Stakeholders
Who uses it directly, and who is affected by what it does?
If the people the agent can harm never appear on the canvas, nobody designs for them.
03Performance Needs
What accuracy, explainability, speed, cost, and other standards must it meet?
Without a testable bar, nothing defines "good enough" before the agent acts.
Authority what it may do
Knowledge what it may know
07Context & Knowledge
What information is available to support its work, and which sources should it trust?
An agent grounded on nothing, or on unvetted sources, cannot meet a serious accuracy bar.
08Memory & Learning
What should it remember, what must it forget, and how will it improve over time?
What persists between sessions is a privacy and liability choice, not a technical detail.
Accountability who answers
09Ownership & Oversight
Who owns the outcome, who oversees the agent, and when must a person intervene?
If no one owns the outcome when the agent errs, the design is not ready to ship.
Cell 04 · the autonomy ladder
Five rungs. Pick the highest one the agent may reach.
Every rung is a pair: what the agent does, and what it leaves to a person. The rest of the canvas is then sized to the rung you chose.
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01
Assist
Returns a bounded contribution: an answer, lookup, summary or calculation.
The human integrates it and completes the work.
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02
Advise
Returns an assessment: frames the issue, compares options, may recommend a path.
The human chooses what action to take.
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03
Prepare
Returns a complete, ready-to-use output: an email, plan, booking or report.
The human reviews it before use or execution.
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04
Decide
Makes a binding choice: the option, the classification, the go or no-go. It does not carry it out.
The human implements the decision.
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05
Execute
Makes and implements the choice: sends, files, books, pays, updates records.
The human steps in only to handle exceptions.
Choose the highest rung the agent reaches anywhere in the scoped workflow. Read it against the unit of work, not the whole job: an agent can sit at execute for the workflow it owns while a person still holds a decision downstream of it. Oxford NHS's Dora executes the follow-up call and the routing that follows it, and only prepares the discharge recommendation, so it sits at execute. Name the unit of work before you pick the rung.
Want to run it in the room? Download the workshop canvas (A1 PDF) →
The canvas coach
A coach, not a grader.
Fill the nine cells, then let a Claude-backed coach press the gaps a good workshop facilitator would: per cell, plus cross-cell contradictions and an overall readiness read.
- Socratic, per-cell feedback. It surfaces gaps, vague answers and danger signals, and never invents facts about your company.
- Cross-cell contradiction flags. A "decide"-level autonomy sitting above an empty Rules cell is a post-mortem waiting to happen, caught now.
- Autonomy-aware. It judges every cell in proportion to the rung you choose in Cell 04.
- Built for the room. Fill from a photo of a hand-drawn sheet, iterate in rounds, export JSON, print a clean PDF handout. Works in English and Japanese.
Nothing here stops the agent once it is wrong. Draw the stop conditions before you raise the rung.
What must this agent never do with a refund, and what forces it to stop and hand the case to a person?
One unresolved question, surfaced before anyone builds.
The cases
Three agents, designed on the record.
Three organisations, three rungs of the autonomy ladder, one canvas. Each is reconstructed from public sources, filled in cell by cell, and then run through the coach.
CoreMate
the thirteenth chair in the boardroom
- assist
- advise
- prepare
- decide
- execute
The AI received a role, not decision authority. It challenges proposals before and during senior meetings, and executives keep the vote.
Open the full canvas → AllianzProject Nemo
the agent that audits the agents
- assist
- advise
- prepare
- decide
- execute
Seven agents settle a claim in minutes. The pipeline calculates the payout but cannot pay it. That one action is withheld from every agent in the system.
Open the full canvas → Oxford NHS · UfoniaDora
the agent on the telephone
- assist
- advise
- prepare
- decide
- execute
Executes the follow-up call and its routing end to end; the discharge decision stays with a clinician. Autonomy earned through published evidence, a tight scope and an explicit hand-off.
Open the full canvas →Good design does not always mean lower autonomy. It means autonomy matched with evidence, boundaries, tools and accountability.
The canvas as a diagnostic
It also reads backwards.
The three agents above show deliberate design choices in the public record. Run the same nine questions over an agent that does not, and the canvas stops being a design tool and becomes a post-mortem.
The compass set to the wrong north.
A little over a year later, it narrowed what the agent was allowed to handle and hired people back for the conversations that needed them.
Bloomberg · May 2025 ↗- Cell 01 · the metricMeasured cost saved and agents replaced, not the quality of the outcome.
- Cell 04 · the rungPlaced at execute: handling two-thirds of all interactions, end to end.
- Cells 06–09 · the controlsBlank. No never-do list, no way to detect a distressed customer, no owner.
How to run it
Five moves. Half a day.
The canvas is built to be worked, not read. Answer its nine questions in half a day now, or discover them the hard way after you ship. In real cases, that has meant months of rework, a public reversal, or an apology to a federal judge.
Print it large, fill the room
Put it on an A1 sheet. Gather product, engineering, design, legal or risk, and someone who does the work the agent will touch.
Start with the job. Always.
Anchor on the workflow, the people it serves and the standard it must meet, before anyone discusses what the AI does. Teams that start with the technology design a clever agent in search of a job.
Set the authority envelope
Place the agent on the autonomy spectrum, then decide which tools it may reach and where its boundaries lie. Every later cell is sized to the rung you pick here.
Decide what it may know and remember
Which sources it can trust, what it carries from one session to the next, what it must forget. Ground the agent, then set its memory.
Name accountability, then walk the canvas
Say who owns the outcome and when a person must step in. Then walk the whole sheet: the empty cells and the contradictions are the deliverable.
The LLM provides cognition.
You provide control.
For the first time, you have to write it all down before the worker shows up. And the better the model becomes, the more that control is worth. Start on the canvas.
The book
The book behind the canvas.
- The theoryAgent Operating Model
- The methodThe Design Canvas
- The applicationThe coach
- The argumentDon't Prompt, Design
The canvas and the Agent Operating Model are the spine of Don't Prompt, Design, a book I'm writing on building agentic AI an organisation can trust. Leave your email and I'll send the occasional update, new worked examples, and early chapters as they take shape.