@RLanceMartin - this is a nice / simple pattern for agent memory. reflect
Lance Martin✓@RLanceMartin
2025-12-06this is a nice / simple pattern for agent memory. reflect over session logs, distill preferences / feedback from actual use to update memory. been doing this w/ Claude Code for ~1-2 months and very effective. write up + code:
http://rlancemartin.github.io/2025/12/01/claude_diary/
elvis@omarsar02025-10-10Agentic Context Engineering Great paper on agentic context engineering. The recipe: Treat your system prompts and agent memory as a living playbook. Log trajectories, reflect to extract actionable bullets (strategies, tool schemas, failure modes), then merge as append-only deltas with periodic semantic de-dupe. Use execution signals and unit tests as supervision. Start offline to warm up a seed playbook, then continue online to self-improve. On AppWorld, ACE consistently beats strong baselines in both offline and online adaptation. Example: ReAct+ACE (offline) lifts average score to 59.4% vs 46.0–46.4% for ICL/GEPA. Online, ReAct+ACE reaches 59.5% vs 51.9% for Dynamic Cheatsheet. Paper: https://t.co/AZRZe0axlI
