Topic starter
02/08/2026 11:50 am
TL;DR: AI-assisted coding makes the PRD the control surface for implementation, with SafeBreach in the middle of the finding as the work shifts from manual coding to agent-led delivery. Product requirements now govern what gets built, so teams have to keep specifications current to reduce drift and ambiguity.
Editorial analysis by NHI Mgmt Group, based on content published by SafeBreach: “Becoming an AI-First Development Team, Part 3”.
Key questions
Q: Why do coding agents drift from the original plan during longer tasks?
A: They work inside a finite context window, so earlier decisions are summarised or dropped as the session continues.
Q: What mistakes do teams make when using PRDs with AI coding agents?
A: The most common mistake is treating the PRD as a one-time planning note instead of a living execution record.
Q: How should teams govern AI-assisted coding when the model writes most of the implementation?
A: Teams should govern the specification first, then the code.
Practitioner guidance
- Standardise PRD structure for AI-assisted work Use a fixed PRD template with scope, architecture, testing, risks, implementation phases, and change log fields so the agent always has a canonical reference.
- Commit the PRD before implementation starts Treat the PRD as a reviewable repository artefact and confirm the design is complete before any code is generated or merged.
- Update the PRD after each phase After every implementation phase, record the decisions made, open questions, and current state so the next step starts from an accurate baseline.
Bottom line: AI-assisted coding changes the control surface of software delivery, because the specification now governs what the agent builds.
Glossary terms: Control Surface, Context Compaction, Living Specification, Architectural Drift
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This topic was modified 3 days ago by NHI Mgmt Group
02/08/2026 1:00 pm
Specification governance is now a software control problem, not a documentation preference. When AI agents generate code from loosely defined prompts, the real risk is not just inefficiency, but uncontrolled interpretation of intent. The PRD becomes the artefact that governs scope, architecture, and completion criteria before code exists. For security and engineering leaders, this shifts assurance upstream to the quality of the written control surface.
A question worth separating out:
Q: When should a developer split an AI-assisted task into smaller missions?
A: Split the work when the PRD grows beyond what one human-agent pair can keep aligned through implementation, testing, and review. The practical limit is not a line count alone, but whether the agent can still retain scope, risks, and remaining phases without losing fidelity.
👉 Read our full editorial: PRDs are becoming the control surface for AI-assisted coding