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Trace Loops, Not Free Tokens, Are the Real Fix for Debugging AI Agent Runs

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A software developer argues that the true cost of running AI agents lies not in token usage but in the time spent understanding why specific tool calls occurred. Agent-generated summaries are unreliable compared to detailed logs that record each tool call, its arguments, result hashes, and timestamps. The author built a minimal trace collector running on a free server that captures JSON events and stores them in a flat file, deliberately avoiding databases or authentication for simplicity. A companion diff script then compares two trace files to identify tool calls that appeared, disappeared, or changed between runs, helping catch regressions. The setup was developed using MonkeyCode's free tier offering, though the core methodology applies broadly to any AI agent debugging workflow.

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