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To audit agent session transcripts, you compare documented steps and governing instructions against actual behavior. This process identifies non-compliance and reasoning gaps, generating a detailed compliance ledger outlining adherence rates and violations.

Yes, you can analyze JSONL transcripts from Claude Code, Cursor, and Codex. The analysis requires Python, JSONL parsing, and machine learning models to examine the agent's thought process and identify self-rationalized shortcuts.

Agent reasoning analysis examines an agent's thought process to identify self-rationalized shortcuts or degradation over time. It works by comparing documented steps and governing instructions against actual behavior in session transcripts.

Yes, you need Python, pandas, numpy, and JSON parsing to analyze agent session transcripts. These dependencies support the machine learning models required to audit compliance and examine reasoning behavior in JSONL files.

You generate a compliance ledger by auditing agent session transcripts to compare documented steps against actual behavior. The resulting report outlines adherence rates, violations, and recommendations for improvement based on the analysis.

The limitations of using machine learning for session analysis include the requirement for structured JSONL transcripts and dependencies on Python, pandas, and numpy. The analysis is constrained by the quality of the documented steps and governing instructions provided.