I wrote a concept paper draft around a distinction I’ve been thinking about in agent systems.
Main idea: agents may be missing a reusable operational memory layer for things they learn by actually doing tasks over time — distinct from user memory, retrieval/RAG, and fine-tuning.
Examples include:
- tool quirks discovered during execution
- workflow patterns that repeatedly work
- environment-specific process knowledge
- failure modes that are expensive to rediscover
I’m calling the pattern “Agent Experience Cache” for now.
I’m mainly trying to pressure-test:
- whether this is truly a distinct category
- where it overlaps with episodic memory / trajectory storage / tool-use traces
- whether the failure modes and invalidation risks are framed correctly
Main idea: agents may be missing a reusable operational memory layer for things they learn by actually doing tasks over time — distinct from user memory, retrieval/RAG, and fine-tuning.
Examples include:
- tool quirks discovered during execution
- workflow patterns that repeatedly work
- environment-specific process knowledge
- failure modes that are expensive to rediscover
I’m calling the pattern “Agent Experience Cache” for now.
I’m mainly trying to pressure-test:
- whether this is truly a distinct category
- where it overlaps with episodic memory / trajectory storage / tool-use traces
- whether the failure modes and invalidation risks are framed correctly
Draft here:
https://docs.google.com/document/d/126s0iMOG2dVKiPb6x1khogldZy3RkGYokkK16O0EmYw/edit?usp=sharing