Day 21: Agent Failure Modes & Debugging Techniques 🧨🔍

Executive Summary Most teams don’t notice agent failures — they experience symptoms: agents looping endlessly 🔁 confidently wrong answers ❌ unexpected API bills 💸 agents that "work in demos" but fail in production 🚨 Agentic systems fail differently from traditional software and even from standard ML systems. This chapter is about: why agents fail how to detect those failures early how to debug systems that reason, plan, and act autonomously Debugging agents is not about fixing bugs — it’s about correcting behavior under uncertainty. Why Agent Failures Feel So Confusing 😵‍💫 Traditional systems fail because: logic is wrong data is missing infrastructure breaks Agents fail because: reasoning goes off-rails 🧠 goals drift 🎯 assumptions compound feedback loops amplify mistakes The system is doing exactly what you allowed it to do — just not what you intended. That’s why agent debugging feels psychological as much as technical. A Simple Mental Model: Where Can an Agent Break? 🧩 Think of an ag