Debugging Claude Code Agents: Reading Transcripts, Tracing Tool Calls, and Finding Where Your Agent Goes Wrong
This article was written with the assistance of AI, under human supervision and review.
Most agent debugging problems stem from treating AI execution like synchronous code. Developers reach for console.log, step through with a debugger, and wonder why the agent fails in production but works in development. The execution model is fundamentally different: agents make non-deterministic decisions across multiple LLM calls, each influenced by context that changes between runs.
Traditional debugging assumes deterministic behavior. Set a breakpoint, inspect state, reproduce the issue. Agent execution breaks all three assumptions. The same input produces different tool calls. Context windows overflow silently. The model hallucinates field names that don't exist in your schema. By the time the error surfaces, the decision trail that led there is already gone.
The solution requires capturing the complete execution path: every tool call, every model decision, every context state transition. Agents need execution transcripts that show not just what happened, but why the agent chose each action. This distinction is critical. Without the reasoning chain, debugging becomes archaeology—digging through logs to reconstruct decisions that are fundamentally probabilistic.
