Loops Are for Prototypes. Lifecycles Are for Production

Short summary

Loop engineering—wrapping AI coding agents in autonomous control structures—is a powerful prototyping discipline but not a production strategy. Loops excel for spikes and throwaway prototypes where learning speed matters more than correctness, but drift, cost, and self-verification bias make them dangerous for production. The real skill is knowing when to graduate from loops to a proper lifecycle with independent verification and cost controls.

• •Loop engineering is ideal for prototyping but insufficient for production AI agent systems
• •Autonomous loops suffer from drift, high token costs, and self-verification bias
• •The key skill is knowing when to transition from loops to a full SDLC with independent review

Generated with AI, which can make mistakes.