Thinking-Trace Analysis: Methodology for Surfacing Operational Improvements and Behavioral-Contract Candidates from Agent Planning Text

This paper records a methodology for analyzing AI agent thinking traces: the extendedplanning text an agent produces before and during task execution. The methodology uses atwo-pass structure. The first pass is a piece-by-piece temporal pass that extracts operationalimprovements, behavioral-contract candidates, vocabulary coinages, and efficiency patternsper trace segment. The second is a structural synthesis pass that ranks improvementsby leverage and attributes overhead to either the system (form and protocol gaps) or theagent (judgment patterns). The central analytical question is whether observed overheadis structural or behavioral, because the distinction determines the appropriate remediation.The methodology is demonstrated on one trace from an experimental inquest design task.Limitations include single-trace calibration, analyst-dependent segmentation, and invisibilityof absent reasoning.