What mistakes do teams make when using PRDs with AI coding agents?

Why PRDs Break Down When Teams Hand Them to AI Coding Agents

Teams most often make the mistake of treating the PRD as a static planning artifact instead of an execution contract that has to evolve with the work. Once an AI coding agent starts implementing, the document has to absorb decisions, constraint changes, and rejected approaches, or it stops being useful as a control surface for scope, review, and merge readiness.

That matters because AI coding agents move quickly, make broad edits, and can follow incomplete direction literally. If the PRD is vague, stale, or too small to capture the real design, the agent will optimize for apparent progress rather than the intended product outcome. The result is usually drift between what the team approved and what lands in the codebase.

One common failure is under-scoping the PRD. Teams describe the feature at a headline level, but do not specify boundaries, non-goals, acceptance criteria, data handling, error states, rollback expectations, or the exact places where human review is required. An AI coding agent then fills in the blanks itself, and those blanks are often where the expensive mistakes live.

A second mistake is using the PRD only as an input to start work, rather than as the record that tracks the work through each phase. If the implementation discovers a new dependency, a changed API contract, or a safer architectural path, the PRD should reflect that shift. AI Coding Agents Security Guide is useful here because the same documentation discipline that reduces secret exposure and sandboxing mistakes also reduces implementation drift.

A third mistake is failing to reconcile the final code against the approved design before merge. Teams often check whether the code compiles or whether the agent “finished,” but not whether the implemented behaviour still matches the original intent. That final reconciliation step is where teams catch scope creep, hidden shortcuts, accidental overreach, and changes that look small in diff form but are large in production effect.

PRDs also become brittle when they are written as if the agent will infer business context that was never stated. AI coding agents are very capable at producing code, but they are not reliable substitutes for precise product judgement. The more ambiguity you leave in the PRD, the more you shift decisions from explicit review into implicit model behaviour, which is exactly where teams lose control.

Good PRDs for AI coding agents therefore behave less like a one-time brief and more like an execution ledger. They should capture what was asked for, what changed, what was deliberately rejected, and what remains open. That record is what lets humans review the agent’s output against an agreed target instead of against memory or intuition.

How Scope, Phase Updates, and Final Review Fail in Practice

The deepest mistake is not one missing section, it is a broken operating model. Teams often define the feature, ask the agent to build it, and then only look again when something feels off. In practice, the PRD needs checkpoints that align to phases such as design approval, implementation, QA, and merge readiness, because each phase raises different questions and reveals different classes of error.

When teams skip phase-by-phase updates, they lose traceability. A choice that made sense during planning may become unsafe or incomplete after the first implementation pass, but nobody updates the document to reflect that decision. Over time, the PRD and the code diverge, and reviewers no longer know whether they are validating the current plan or the original one.

That divergence is especially damaging when AI coding agents are allowed to make broad edits across files. The implementation can appear coherent locally while quietly violating assumptions elsewhere in the system. AI Agent Authorisation Guide is relevant because scope control and per-action approval are the same discipline, even when the output is code rather than an agent action.

Teams also underestimate how much final reconciliation matters. A merge should not be approved simply because the agent produced a plausible implementation and the test suite passed. The useful question is whether the delivered behaviour still matches the approved PRD, including edge cases, constraints, and exclusions that the agent may have filled in creatively.

This is why the most effective PRDs include explicit review criteria. They make it possible to say, “this behavior is expected,” “this behavior is out of scope,” or “this change requires a revised decision before merge.” Without that, the review conversation becomes subjective and the agent’s output can be accepted because it is good enough rather than correct.

What a Practical PRD Workflow Looks Like for AI Coding Agents

The practical fix is not to write longer PRDs for their own sake. It is to write PRDs that can survive implementation. That means separating goals, constraints, accepted trade-offs, and outstanding questions, then updating those sections as the agent discovers real implementation details.

A workable process is to treat each phase as a checkpoint with a small decision set: does the implementation still fit the approved scope, does any new dependency alter the design, and does anything now require human sign-off before merge? If the answer changes, the PRD changes with it. The document is not a log of intent at kickoff, it is the current contract for the work.

Teams should also keep the PRD close to the review flow. When a PRD sits outside the development loop, reviewers end up comparing code to memory, chat history, or a stale ticket. When the PRD is maintained as the source of truth, review becomes much sharper because the team can compare implementation, approved design, and final outcome in one place.

AI Agent Observability, Audit and Incident Response Guide is a useful companion to this workflow because the same need for traceability applies whether you are reviewing an agent’s actions or a coding agent’s edits. If you cannot reconstruct what changed and why, you cannot trust the result.

For teams deciding how strict to be, the right rule is simple: the more autonomy you give the agent, the tighter the PRD discipline has to become. Loose requirements and autonomous code generation do not mix well. The safest pattern is to increase specificity, increase review points, and require explicit reconciliation before merge whenever the agent can affect production behavior.

Practitioner Guidance

What to prioritise: Treat the PRD as a living control document, not a kickoff note. The first improvement is usually not more detail everywhere, but clearer boundaries, explicit non-goals, and a named owner for updates as the agent progresses.

What to verify: Before merge, verify that the implemented behavior matches the approved PRD, not just the latest patch or test result. If the code solves the problem in a different way, require the PRD to be updated or the implementation to be corrected.

Common mistake: Teams often confuse “the agent delivered code” with “the team approved the outcome.” Those are different events, and the second one still needs a human decision against an up-to-date specification.

Practitioner takeaway: With AI coding agents, the PRD is only useful when it stays synchronized with execution; once that link breaks, review quality drops and scope drift becomes the default.

Related resources from NHI Mgmt Group

• Why do teams need to scan the running application instead of only reviewing source code when using AI coding agents?
• What do security teams get wrong about using general-purpose AI coding agents for vulnerability remediation?
• What mistakes do teams make when connecting AI agents to API security systems through MCP?
• How should security teams make NHI best practices usable across the business?