How Much Memory Does Your Agent Actually Need?

IBM Research reports that ALTK-Evolve enables agents to distill reusable behavioral guidelines from their own successful and unsuccessful task trajectories and reintroduce those guidelines at inference time without model fine-tuning or human annotations. Tests across eight models on the AppWorld benchmark found that the optimal amount of memory varies by model capability: strong models with remaining performance headroom benefited most from receiving complete guideline sets, weaker models performed better with a compact core and task-specific retrieval, and some already high-performing models showed no measurable improvement. For example, gpt-oss-120b improved task completion by 16.1 percentage points with curated retrieval while adding only 5% more tokens, whereas DeepSeek-V3.2 gained 9.5 points from full-memory injection, with larger gains on the stricter scenario-completion metric. Full guideline sets can substantially increase token usage because they are resent during agent steps, but prompt caching may reduce production costs by reusing static instructions. The researchers conclude that agent memory should be calibrated rather than simply accumulated and identify learned retrieval selectors, memory support for very weak models, broader benchmarks, and controlled studies of context-window effects as future work.

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