Give an AI agent a PRD as a decision document, not as a command to build everything immediately. Attach the current version, identify the feature slice in scope, and ask the agent to turn it into a reviewable implementation plan before changing code.
Prepare the handoff with these elements:
- Problem and user: What problem is being solved, and for whom?
- Scope: Which requirement IDs belong to this task?
- Non-goals: What should remain out of scope, even if it appears related?
- Constraints: Which stack, interfaces, policies, and deadlines shape the solution?
- Acceptance: What behavior, tests, analytics, or review evidence proves completion?
- Dependencies: Which teams, services, migrations, or decisions can block progress?
Tell the agent to return four things before implementation: its interpretation, unresolved questions, assumptions it proposes to make, and an ordered plan. This catches gaps such as an undefined permission rule or missing failure state while changes are still cheap. For a large PRD, select one vertical slice instead of asking the agent to execute the whole document in one run.
Keep the PRD authoritative. If implementation reveals a conflict, the agent should identify the affected requirement and request a decision rather than silently rewriting product intent. Once you approve the plan, use the PRD's requirement IDs in commits, tests, and review notes. That creates a traceable path from product decision to verified code.
Related reading: What makes a good AI coding-agent prompt? and What is Plan Mode in an AI coding agent?.
