Debugging Agents
definition
Debugging agentic systems requires fundamentally different approaches from debugging traditional software because agent behavior is non-deterministic, multi-step, and depends on both the model's reasoning and the tool responses it receives. The primary debugging workflow involves inspecting traces (the full sequence of thoughts, actions, and observations), comparing expected versus actual tool call parameters, and identifying where the agent's reasoning diverged from the intended path.
Debugging agentic systems requires fundamentally different approaches from debugging traditional software because agent behavior is non-deterministic, multi-step, and depends on both the model's reasoning and the tool responses it receives. The primary debugging workflow involves inspecting traces (the full sequence of thoughts, actions, and observations), comparing expected versus actual tool call parameters, and identifying where the agent's reasoning diverged from the intended path. Unlike traditional debugging where you set breakpoints and step through deterministic code, agent debugging often involves replaying the same prompt and getting different behavior — making trace logging and structured observability essential rather than optional. The most common debugging patterns include reducing the problem to the minimal failing prompt, testing tool calls in isolation, and systematically varying the context to identify which piece of information is causing the model to go off track. This concept connects to trace analysis for the structured approach to reading agent traces, observability platforms for the infrastructure that captures debuggable data, and eval-driven development for proactively catching issues before they reach production.