23 articles in AI engineering
Every new model release prompts the same question — is this the one that finally makes AI coding agents reliable? It's the wrong question. What keeps single-agent workflows from scaling to production is architectural, not about the model. Here's the pattern the teams shipping real code keep converging on.
Gene Kim and Steve Yegge argue that vibe coding works for production — if you have preventive, detective, and corrective controls. We agree. We built the platform that enforces those controls structurally. Here's what we've learned about why practices alone aren't enough.
Vibe coding is productive, inevitable, and here to stay. But without guardrails — prevention, detection, and correction — it creates tech debt at warp speed. The teams that win will be the ones who vibe code with discipline, shipping fast AND shipping safely.
Your team adopted GitHub Copilot, spent $180K on licenses, and expected faster delivery. Instead, lead times got longer, defect rates climbed, and your senior engineers are spending more time reviewing AI-generated code than writing their own. Here's why — and what the fix actually is.
AI coding agents don't create quality — they amplify whatever's already there. The 2025 DORA Report confirms it: AI adoption correlates with higher instability. Here's why your codebase determines whether AI accelerates delivery or accelerates chaos.