Building Guardrails for AI Coding Systems
summary
Engineering teams face rapid AI code generation—Sonar finds 42% of committed code is AI‑generated (6% in 2023, projected 65%) while 96% of developers distrust it but only 48% validate. First‑pass correctness sits around 50–60%. The recommended approach is the AI Validation Pyramid: automated type checking and linting, followed by unit and end‑to‑end tests (Playwright, Chrome MCP, Vercel agent browser CLI), then focused human review; following the PIV loop raises reliability toward ~90% and catches CWE‑20 input issues.
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chapters (12)
- 0:00Introduction
- 0:48Why AI-Generated Code Is a Growing Challenge
- 1:46The Trust Problem: High Distrust, Low Validation
- 3:11Why Better AI Models Won’t Solve This Alone
- 4:37Two Core Problems Teams Must Address
- 6:57The AI Code Validation Pyramid Explained
- 10:29Type Checking and Linting as the First Layer
- 12:05Unit Tests and End-to-End Validation
- 16:20The Human Role in Final Code Review
- 22:03Test-Driven Planning and Structured Specs
- 27:52Sizing Tasks and Building a Working Loop
- 31:47Key Takeaways and Where to Learn More
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