Part 3: Hardening Your AI Safety Net – Code Audit Automation
While manual reviews catch many issues, these automated systems will intercept 92% of AI-generated risks before they reach production (2024 DevSecOps benchmark data). Here’s how to implement them: 1. Semantic Code Firewalls Problem: Traditional linters miss AI-specific anti-patterns like: Over-optimized unreadable code “Clever” but dangerous shortcuts Hallucinated
Read More
Part 2: AI Code Review Checkpoints – Where Human Intervention is Non-Negotiable
While AI-generated code accelerates development, these 5 critical checkpoints ensure it doesn’t compromise stability or security. Implement them to maintain velocity without sacrificing quality. 1. Pre-Commit: The First Line of Defense What to vet: High-risk areas (auth, payments, data processing) Third-party dependencies (check for vulnerabilities via npm
Read More
Part 1: 3 Real Cases Where AI Agents Broke Production
AI agents accelerate development, but blind trust can lead to catastrophic failures. Here are three real-world examples (based on 2024-2025 incidents) where AI-generated code caused critical production outages. Case 1. The “Optimized” API That Killed Payments What happened: A startup team used GitHub Copilot to refactor their payment
Read More
AI Agents in Development: How to Maintain Control Over Code and Architecture?
When AI Becomes a Problem, Not an Assistant Imagine this: Your AI assistant generates hundreds of lines of code in minutes, completing a task that would take a developer half a day. Everything looks perfect… until the first production bug hits. It turns out the AI used
Read More