Compiling Successful Agent Trajectories into Procedural Memory
robh1540 · reddit · 2026-07-20
A Reddit user initiated a discussion exploring why AI agents don't learn after successfully completing tasks. The author proposes a concept:
- Experience Extraction: Upon task completion, another model inspects tool calls and outputs to extract and cache reusable "problem → strategy" pairs.
- Memory Injection: When encountering similar tasks, retrieve the strategy and inject it into the agent's context.
- Cost Optimization: This effectively compiles successful trajectories into procedural memory. High-quality models can generate these via self-play, while cheaper models can be used at runtime.
The author asks if any teams have actually implemented this mechanism in production environments and what engineering challenges they faced.
Related event: Industry Discusses Procedural Memory for AI Agents(2 posts)→
More from coding & agent
- Is inference latency becoming the biggest bottleneck for production AI agents? — Euphoric_Sea632 · 2026-09-11
- Anthropic researcher: 99% of engineers now run swarms of 300+ self-improving agents — AlishaOutridge · 2026-09-11
- Gergely Orosz: Shipping 10x PRs With AI Agents, Sites Fill With Small Regressions — ducha_aiki · 2026-09-11
- Same Echo Maze prompt, three frontier models: all passed visually but shipped the same hidden bug — eyishazyer · 2026-09-11
- Astra storyboards plus Minimax H3 per-shot generation boost video success rates — Hailuo_AI · 2026-09-11
- Codex tip: use Sol with Astra and Luna sub-agents to save usage — pvncher · 2026-09-11