Quick answer
A PRD for AI agents is a product requirements document stripped of ambiguity. Keep the same bones as a traditional PRD — overview, users, user stories — but make every requirement testable, enumerate edge cases instead of implying them, and state your non-goals explicitly so the agent doesn't invent features. The PRD is the first document in the spec stack; get it right and your architecture, API spec, and database schema all inherit a clean foundation.
In this guide
1. How an AI PRD Differs from a Traditional PRD
A traditional PRD is written for humans who share context. When a PM writes "users can reset their password," an engineer fills in the obvious details: rate limits, expiring links, what happens on a bad token. An AI coding agent has none of that judgement. It follows the document literally, and anything you leave implicit, it either guesses or ignores. A PRD for agents has to close those gaps on the page.
| Traditional PM PRD | PRD for AI agents | |
|---|---|---|
| Reader | Humans with shared context | A literal agent, no context |
| Acceptance criteria | Prose, often implied | Explicit, testable conditions |
| Edge cases | Assumed, filled in later | Enumerated on the page |
| Out of scope | Rarely stated | Stated so nothing is invented |
| Ambiguity costs | A clarifying Slack message | A wrong feature, built confidently |
The core shift: a human PRD can afford to be suggestive because a person resolves the gaps. An AI PRD has to be prescriptive, because the agent resolves gaps by guessing — and it guesses confidently.
2. The Template, Section by Section
Each section below removes a specific class of ambiguity. Keep them in this order — it moves from the "why" down to the hard constraints an agent needs before it writes a line of code.
Overview / Problem
Why this exists and what problem it solves. One paragraph of context so the agent understands intent, not just mechanics.
e.g. "Teams lose track of action items after meetings. Build a tool that extracts tasks from meeting notes and assigns owners."
Goals & Non-Goals
What success looks like — and explicitly what this feature will not do. Non-goals stop the agent from scope-creeping.
e.g. Goal: "Extract tasks with 90%+ owner accuracy." Non-goal: "We are not building calendar scheduling in this release."
Users & Personas
Who uses this and what they need. Grounds decisions about permissions, defaults, and flows.
e.g. "Team lead: assigns and reviews tasks. Member: views only tasks assigned to them."
User Stories
Standard "As a [role], I want [action] so that [benefit]" statements. Each story maps to acceptance criteria below.
e.g. "As a team lead, I want to reassign a task so that I can rebalance work when someone is out."
Acceptance Criteria (testable)
Explicit, verifiable conditions that define "done" for each story. These are the agent's success criteria — write them as checks.
e.g. "[ ] Reassigning a task notifies the new owner within 60 seconds."
Edge Cases
What happens when things go wrong or sit at a boundary. Enumerate them — the agent will not infer them for you.
e.g. "Reassigning to a user who left the team → block and show an error."
Constraints & Assumptions
Technical limits, dependencies, and anything taken as given. Keeps the agent inside your stack and reality.
e.g. "Must run on the existing Postgres instance. Assume all users are already authenticated."
Out of Scope
Features deliberately excluded. The single most effective section for stopping an agent from building things you did not ask for.
e.g. "No mobile app. No third-party integrations. No bulk import in this version."
3. A Worked Example You Can Copy
Here are a couple of filled-in sections for a "task extraction" feature, written the way an agent can act on directly. Copy the shape — explicit criteria, enumerated edge cases, stated non-goals — and fill in your own feature.
## Feature: Extract Tasks from Meeting Notes
### User Story
As a team lead, I want tasks extracted automatically
from pasted meeting notes so that nothing gets lost
after a call.
### Acceptance Criteria
- [ ] User can paste plain-text notes into a textarea
- [ ] System returns a list of tasks, each with a title
and a suggested owner
- [ ] Owner is matched against existing team members
by name; unmatched owners are left blank
- [ ] User can edit any extracted task before saving
- [ ] Saving persists tasks to the current workspace
### Edge Cases
- Empty notes -> show "No tasks found", save nothing
- No owner detected -> leave owner blank, do not guess
- Duplicate task text -> keep one, flag the duplicate
- Notes over 10k chars -> truncate with a visible warning
- Owner name ambiguous -> list candidates, ask user to pick
### Non-Goals
- Not transcribing audio in this release
- Not scheduling tasks to a calendar
- Not sending notifications (separate feature)
### Priority: P0 (MVP)Notice there is nothing for the agent to interpret. "No owner detected" has a defined behaviour. The 10k-character boundary is named. The non-goals rule out three features the agent might otherwise add "to be helpful." That precision is the whole point. For more on what a strong PRD contains, see the AI PRD generator guide.
4. How the PRD Anchors the Spec Stack
The PRD is step one of spec-driven development — the opinionated, battle-tested system Keeborg built for AI coding agents before the industry settled on the name. It's the first document in the stack, and every document after it traces back to the requirements and acceptance criteria it defines.
PRD
Defines what to build: user stories, testable acceptance criteria, edge cases, and non-goals. The source of truth everything else references.
Architecture
How the system is structured to satisfy the PRD — components, data flow, and service boundaries derived from the requirements.
API Specification
Endpoints and contracts that implement the user stories. Each endpoint should map back to an acceptance criterion in the PRD.
Database Schema
The data model behind the features. Entities and relationships fall out of the personas and stories the PRD describes.
Why order matters: an ambiguous PRD doesn't stay contained. Every downstream document inherits its gaps, so the architecture and API spec amplify the same guesswork. Fixing the PRD first is the cheapest place to remove ambiguity. You can generate the whole stack at once with the full spec generator.
5. Best Practices (and a Faster First Draft)
The template gives you the shape. These habits make the content hold up when an agent runs against it:
If you can't phrase it as a condition that is either true or false, it isn't an acceptance criterion yet — it's a wish.
Empty input, bad input, boundaries, race conditions, permission failures. If it's not on the page, the agent won't handle it.
The fastest way to stop scope creep is to name what you are deliberately not building this release.
Not "fast" — "responds within 500ms." Not "secure" — "requires an authenticated session." Agents can't act on adjectives.
Store it as prd.md in a docs/ or specs/ directory so the agent reads it directly and it stays version-controlled with the code.
Writing it by hand
- Hours per feature before any code
- Edge cases and non-goals get skipped
- Formatting drifts between features
- Easy to leave half-finished
Generate, then refine
- Structured first draft in seconds
- Edge cases surfaced systematically
- Consistent format every time
- You spend your time refining, not typing
Writing these by hand is slow, and the parts people skip — edge cases and non-goals — are exactly the parts an agent needs most. The faster path is to generate a structured first draft and refine it. The free PRD generator gives you the whole template filled in from a plain-English description in under 90 seconds.
Generate a PRD free
Describe your feature in plain English and get a structured PRD — user stories, testable acceptance criteria, edge cases, and non-goals — in under 90 seconds. The whole template, filled in.
Try the free generatorGet the whole spec stack
The Keeborg dev system generates the PRD and the documents it anchors — architecture, API spec, database schema — kept consistent with each other, plus the quality gates and continuity that keep agents on spec.
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