control-loop-extraction

To extract an agent reasoning loop, point the analysis at the framework's agent engine entry points to map the step function, input assembly, LLM invocation, and dispatch logic. This process reveals core decision-making patterns and termination conditions.

This supports identifying ReAct, Plan-and-Solve, Reflection, and Tree-of-Thoughts reasoning patterns. It maps the step function and termination conditions across various codebases to reveal core decision-making architectures.

Comparing agent loop designs involves extracting the step function and termination conditions from multiple framework codebases. This maps input assembly and dispatch logic, enabling faster architecture comparisons and informed framework decisions.

Yes, you can use this to find termination guards by analyzing the agent's step function. It ensures the reasoning loop has clear termination conditions mapped alongside the output parsing and dispatch logic.

Mapping the control loop of an AI agent helps teams quickly locate core decision-making patterns, enabling faster debugging. It reveals how input assembly and LLM invocation interact within the step function.

This applies to multiple framework patterns including ReAct, Plan-and-Solve, Reflection, and Tree-of-Thoughts. It analyzes various codebases to map the step function and dispatch logic regardless of the specific implementation.