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)→
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