self-reflection

Agent memory and retrospective learning work by capturing mistakes during execution and writing them to a memory file. A heartbeat timer prompts regular reflection checks, logging lessons so the agent retrieves past learnings to avoid repeating issues.

A heartbeat-driven reflection check is a timer-based mechanism that prompts an agent to pause at regular intervals during long-running workflows. It evaluates recent actions, logs mistakes, and updates a persistent memory file to maintain continuous improvement.

Yes, you need jq and date installed to configure agent memory logging. These dependencies are required to track timer states, format timestamps, parse memory files, and retrieve logged reflections during the retrospective workflow.

To set up a post-mission retrospective, configure a memory file and heartbeat timer state for your agent. After mission completion, the agent logs execution mistakes and feedback into the memory file to build institutional knowledge for future tasks.

You should implement a memory-augmented reasoning loop instead of standard logging when your agent requires adaptive behavior based on prior learnings. This structured approach captures retrospective feedback for continuous improvement rather than just recording flat event logs.