Agent Runs Are Black Boxes: A Trace-First Debug Loop

The article outlines a practical approach for debugging AI agents that interact with tools, using traces as the primary method of observation and analysis. Here’s a summary of the key points:

Key Components

1. Agent Loop: A loop that repeatedly calls an AI model to generate actions (tool calls) until it reaches a goal or runs out of tokens.
2. Tool Calls: The agent invokes specific tools using their names and arguments, which are recorded in the trace.
3. Traces: JSONL files containing events like tool calls, results, and run status that capture the entire interaction history.

Methodology

1. Agent Loop Implementation:

   • Initialize a loop with an initial prompt or task description.
   • Continuously call the AI model to generate actions (tool calls).
   • Record each action in a trace file using structured events like tool_call, tool_result, and run_end.

2. Tool Execution:

   • Dispatch tool calls based on the generated instructions from the model.
   • Capture the outcome of these tools, whether successful or not.

3. Trace Analysis:

   • Use shell commands to analyze traces for debugging purposes:
     • Identify steps where a specific tool

Read the full article at DEV Community

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