How I Review AI Code: Self-Healing Code Agents | Google Engineer | AI-DLC

Stop wasting time on manual code reviews. Learn how to build an agent skill that automates your preferences into reusable rules. Github Repo: https://github.com/buildwithdc/agenti... AI code generation is fast, but the review process often becomes a bottleneck that slows down your production cycle. This video covers the trade-offs of using LLMs for development and introduces a practical framework for distilling your personal coding preferences into an agent skill. By effectively teaching your tools how you prefer to work, you can reduce the time spent babysitting model output. We focus on the practical application of agentic coding within a modern engineering workflow. You will learn how to transition from simply generating code to creating a system that understands your specific architectural constraints and style requirements. This approach helps maintain high standards without the constant manual overhead that typically accompanies AI-assisted development. Subscribe for weekly engineering workflow breakdowns, and comment below if you have started building your own custom agent rules yet. 00:00 - The AI Verification Tax 00:52 - Installation: Set-and-Forget Setup 03:02 - Adaptive Customization & Preference Persistence 05:14 - Automated Quality Gates & Shift-Left Verification 06:18 - The 3-Tier Guardrail Hierarchy (Org, Team, Personal) 07:45 - Git Pre-Commit Hooks & LLM Verification 08:52 - Autonomous Auto-Remediation & Self-Healing Code 09:37 - Zero-Dependency Portability & Multi-Assistant Support 10:06 - Latency, SHA-256 Caching & Graceful Degradation 11:20 - Architecture Recap & Getting Started Follow me on LinkedIn -   / derick-chen   Read more at https://buildwithdc.substack.com/ More resources and guides available at https://www.BuildWithDC.co Systems Atlas (https://atlas.BuildWithDC.co) - a challenge based systems design knowledge map. AI Driven Designer (https://design.BuildWithDC.co ) - AI led software architecture design tool.