Loop Engineering 101: Building Reliable, Self-Correcting AI Workflows

The hardest part of building production AI systems is no longer writing the initial prompt it is managing what happens next. When autonomous agents operate in multi-step workflows, minor inaccuracies compound into catastrophic errors, context drifts, and loops spin endlessly without reaching a solution. Loop Engineering 101 introduces the foundational principles of designing, supervising, and bounding agentic feedback loops. This session breaks down the core anatomy of an effective AI loop . Prompt -> Action -> Verification -> Correction and shows why robust verification is the real engine of agent autonomy. In this talk, you will learn about: The essential anatomy of iterative agent architectures and how state flows between iterations. Deterministic vs. Semantic Verification Convergence & Stopping Conditions Human-in-the-Loop Thresholds