session-logging

You can capture structured agent session logs by running an initialization script that creates timestamped JSON files, enforcing standardized naming conventions and required fields like log_type, change_id, and session_summary for every spawned agent.

Required fields for a standardized agent session log include log_type, timestamp, change_id, iteration, session_summary, decisions_made, issues_encountered, and notes to ensure consistent, auditable session records across workflows.

You generate a timestamped JSON log file by running the automated init-session-log.py script, which creates a properly named log under the change's logs directory using the YYYYMMDD_HHMMSS_session.json format.

The best way to maintain auditable session records is to enforce a standardized log file naming convention and content guidelines for every agent spawn, capturing input/output artifacts, librarian queries, and key decisions with rationale.

Yes, session logging is applicable during every agent spawn across stages such as intake, task generation, software execution, QA, and lessons optimization, ensuring consistent records throughout the entire workflow.