What happens when your autonomous agent gets stuck in a loop? Or when an API changes and your agent starts hallucinating errors? In traditional software, we have try/catch blocks. In Agentic workflows, we need "self-healing" logic.
This talk focuses on the "unhappy paths" of AI automation. I will share specific design patterns for error handling in n8n and Clay, showing how to build "supervisor agents" that monitor the primary agents for failure. We will discuss techniques for implementing circuit breakers in your AI workflows, retry logic that doesn't burn your token budget, and how to gracefully degrade service when the LLM is confused.