reasoning-trace-optimizer

To debug a failing AI agent using its thinking traces, you capture the interleaved thinking blocks and tool calls to identify context degradation, tool confusion, and instruction drift with concrete evidence from the recorded session.

Context degradation is a pattern where an AI agent loses track of instructions or relevant data over time. It affects agent reliability by causing instruction drift or incorrect tool use, which you can diagnose by analyzing interleaved thinking traces.

You optimize prompts based on agent tool-use reasoning by analyzing the captured traces to find errors, then generating concrete prompt improvements and reusable Agent Skills to correct the behavior and support continuous improvement.

Yes, you can generate reusable skills from an agent debugging analysis. The process produces evidence-based recommendations and artifacts, including optimized prompts and shareable Agent Skills for team-wide adoption.

The best way to identify tool confusion in AI agents is to capture and analyze their thinking traces. This exposes the interleaved reasoning behind tool calls, providing concrete evidence of when the agent selects or applies the wrong tool.