Conclusion
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In this lesson, you:
- Moved agentic code review from a working experiment to a governed production practice.
- Applied path filters, drafted pull request exemptions and concurrency controls so that token consumption scales with risk.
- Placed the review jobs as peers inside a multi-job iOS continuous-integration workflow under least-privilege permissions.
- Most importantly, you closed the feedback loop: triage outcomes were captured, analyzed, and translated into concrete updates to the shared guidance file and agent prompts, producing a measurable improvement in signal-to-noise ratio on subsequent changes.
The central takeaway is that the long-term value of agentic review depends more on the surrounding system of permissions, observation and iterative refinement, and less on any single model. When that system is in place, the automated channel remains a consistent quality amplifier rather than a source of noise or unbound.
The same governance and feedback practices will remain applicable as new agents appear and as the team’s own conventions evolve.