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Written by Max Zeshut
Founder at Agentmelt
A detailed, step-by-step log of everything an AI agent did during a single task execution—each LLM call (prompt and response), every tool invocation (input and output), branching decisions, retry attempts, and the final result. Traces are the primary debugging tool for production agents: when a ticket is resolved incorrectly, the trace shows exactly where the agent went wrong—bad retrieval, misinterpreted intent, or faulty tool output.
A support agent misclassifies a billing question as a technical issue. The trace reveals: the intent classifier returned 'technical' with 52% confidence (just above the threshold), routing the ticket to the wrong workflow. Adjusting the threshold from 50% to 60% and adding a billing keyword boost fixes the routing.