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AI Code Reviewer implementation

Ticket #267: AI Code Reviewer implementation
Type: Quality / Governance / Automation
Affected Component: .github/agents/, .specify/scripts/powershell/, src/pr_reviewer/, tests/pr_reviewer/, specs/009-ai-code-reviewer/

1. Context

Following my brainstorming session on an agentic SDLC, I made a key decision: to retain Speckit as the sole agentic system for the structural development of this application. In line with this, I undertook the creation of an AI agent dedicated to code review, designed according to the Speckit approach, in order to integrate this quality assurance step naturally and consistently into my current production process.

2. Objective

Set up an AI review agent that helps make a clear decision:

  • either the Pull Request can be approved,
  • or it must be corrected before merge,
  • with an explanation that is easy to read and act on.

3. What was delivered

  • A structured review flow centered on the /speckit.review agent.
  • A systematic assessment across 5 axes:
    • compliance with the project constitution,
    • security risk level,
    • architecture alignment,
    • test coverage,
    • adherence to planned scope.
  • Automatic publication of a GitHub review outcome:
    • approval (APPROVE) when risk level is acceptable,
    • documented blocking when a critical gap is detected.
  • A replacement mechanism for prior reviews on the same PR to keep one clear final decision.

4. Business impact

  • Lower regression risk before production release.
  • More reliable merge decisions through one consistent quality standard.
  • Coordination time savings thanks to a pre-structured review outcome.
  • Improved traceability of quality decisions at PR level.

5. Validation and status

  • Review and publishing modules were tested locally.
  • Core behavior is validated (classification, report format, review publication).
  • Linked traceability issue is created: #267.

6. Lessons learned

  1. A simple and stable decision framework speeds up merge arbitration.
  2. The main value of AI here is consistency of analysis, not complexity.
  3. Traceability (issue + PR review) is essential for governance and audit.