PAOVR Loop: A Five-Stage Control Pattern to Make AI Agents Actually Finish Tasks

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A technical guide published on DEV Community introduces the PAOVR Loop — Plan, Act, Observe, Verify, Repair — as a production-grade control pattern for building reliable AI agents. The author argues that even in 2026, most agents fail not because the underlying model is weak, but because there is no enforceable definition of 'done' built into the system. Common failure modes include agents marking tasks complete after a single tool call without rechecking original success criteria, or hallucinating results that were never actually produced. The PAOVR framework builds on earlier paradigms like ReAct and Plan-and-Solve by adding a hard Verify-then-Repair gate that prevents incomplete work from being declared finished. The guide includes production prompts, JSON contracts, TypeScript interfaces, and a one-week implementation plan targeting stacks built on Next.js, Supabase, and Vercel.

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