Ideas Weave Every Narrative with AI.
We evaluate AI coding agents by their final output, but that’s a trap. While TDD might not improve an AI’s final code quality, it’s the only invisible safety rail we have. It turns a black-box process into an auditable journey, giving us the control we desperately crave.
AI agents are writing code faster than ever, but that speed comes with a hidden cost: context loss. The real bottleneck isn’t implementation — it’s trustworthy verification. The solution isn’t more reviews; it’s using TDD as a forcing function. Write the failing test first, let the agent earn your trust. Or risk deploying code that passes CI but fails your users.
AI coding speeds up implementation but also amplifies ambiguous requirements, turning unspoken assumptions into working features that must be torn down. The real bottleneck isn’t code—it’s clarity. Product managers must become intent maintainers, using SDD and TDD to create tight feedback loops that catch errors before they become expensive rework.
The secret to unlocking AI coding tools isn’t better prompts. It’s enforcing classical software engineering processes — TDD, code review, bug diagnosis loops — as executable constraints. Matt Pocock’s 180k-star GitHub repository shows how to turn your AI assistant from a messy intern into a disciplined senior engineer. Stop trying to make AI faster. Make it slower, on purpose.