AI assisted code reviews Quality and Security at Scale
summary
Microsoft scales an AI review assistant to touch 90% of pull requests and 95% of developers, reporting a 10–20% faster PR completion time and a 50–60% usefulness rating. Implementation details include prompt version control, an SLM filter to reduce noise, per-repo custom prompts, semantic-search context retrieval, language-specific and multi-agent orchestration (review + critic), an apply change feature for one-click fixes, AB testing for models, guardrails, emergency rollback, and human-in-loop accountability.
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chapters (11)
- 0:00Introduction to AI code reviews
- 1:13Scaling AI reviews at Microsoft
- 3:45AI review workflow and features
- 5:09Early pilot and feedback collection
- 7:15Refining AI suggestions and integration
- 9:20Building trust and reducing noise
- 10:24Customization and multi-agent system
- 12:33Security, guardrails and human oversight
- 14:39Key principles for success
- 15:45Future of AI-assisted code reviews
- 17:32Closing and Q&A
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