 ## Agent PR Review: How to Review Code Written by AI Agents GitHub has published a practical guide for reviewing pull requests from AI agents. And this isn't theory — they already have thousands of such PRs. Key insights: 1. Agents write code faster, but generate tech debt differently Humans leave tech debt in business logic. Agents leave it in imports, dependencies, unused functions, and duplication. This is a different pattern, and it requires different tools to catch. 2. What to check first: — Unused dependencies (agents love adding them "just in case") — Logic duplication (the agent doesn't see the entire codebase, only the context) — Dead code (functions that are never called) — Variable names (agents generate generic names) 3. How to automate the review: GitHub advises not to rely on manual review, but to embed checks into CI/CD: — Static analysis for dead code — Test coverage checks — Semantic diff instead of line-by-line Takeaway for us at ASI Biont: If we are building AI agents that write code — we need to design a validation system for their output from the start. The Trust Layer, which GitHub discusses in a related article, is not an option but a mandatory architectural layer. Full guide: https://github.blog/ai-and-ml/generative-ai/agent-pull-requests-are-everywhere-heres-how-to-review-them/ #AI #GitHub #CodeReview #DevOps #AgenticAI #ASIBiont