SourceHow Much Memory Does Your Agent Actually Need?
Developers can increase AI agent accuracy and reduce operational costs by tailoring the volume of guidance to a model's strength rather than using a one-size-fits-all approach.
- Evaluated ALTK-Evolve across eight models from 30B dense models to frontier proprietary systems on the AppWorld benchmark (585 multi-step tasks).
- Strong models with headroom such as DeepSeek-V3.2 improved task completion by +9.5 percentage points with full self-mined guideline sets.
- Weaker models such as gpt-oss-120b achieved a +16.1 percentage point task completion gain with curated retrieval while adding only +5% token overhead.
- Saturated models like GLM-5 showed no measurable gain from added agentic memory.
- Prompt caching was identified as a key mechanism to keep full guideline sets affordable in production environments.