How to Write PRDs for AI Coding Agents
Your next PRD will be read by humans and executed by AI. Here is what changes about the writing, and what stays exactly the same.
The reader changed
A PRD used to be a conversation starter. An engineer read it, noticed what was missing, and asked. The document could afford gaps because a human filled them.
AI coding agents don't ask. They take your words as the complete instruction set and build exactly what the words say. Every ambiguity becomes a decision the agent makes for you, silently, at speed. That is the single most important shift: the spec is no longer a reference for the build, it is the input to the build.
What an agent-ready PRD contains
The good news: an agent-ready PRD is not a new document type. It is a normal PRD held to the standard PMs always aspired to, with a few points of emphasis:
- Explicit context: the problem, the user, and the constraints, stated in the document rather than assumed. Agents don't attend your standups.
- Decisions, not options: "support both" and "TBD" are instructions to an agent, and it will implement them. Resolve or explicitly exclude.
- Boundaries and non-goals: say what is out of scope. Agents over-build when the edges are fuzzy.
- Concrete acceptance criteria: per requirement, in checkable terms. This is what turns a wish into an executable statement.
The failure mode is vagueness
When GitHub studied more than 2,500 agent instruction files from public repositories, the lesson it published wasn't about format or length. In GitHub's own words: “Most agent files fail because they’re too vague.” The same is true of PRDs handed to AI workflows: the model fills every gap with a plausible guess, and plausible guesses compound into the wrong product.
The practical test: could a competent stranger, with no access to you, build the right thing from this document alone? If not, an agent can't either.
A workflow that produces them
Nobody writes complete documents by staring at a blank page harder. The reliable way to reach completeness is interrogation: someone or something asking you the questions you didn't think to answer.
This is the workflow Wisary is built around. Guided questions extract the context in your head, every AI suggestion arrives as a reviewable diff you approve, and Insights scores the document against quality metrics before you hand it to anyone, human or AI. The finished spec exports as clean, structured Markdown, the native language of AI coding tools.
Write the PRD as if the reader will do exactly what it says. Increasingly, that is precisely what happens.