Agent memory isn't a switch — it's a dose you tune per model
IBM Research's ALTK-Evolve distills reusable guidelines from an agent's own trajectories and injects them at inference, with no weight updates or human labeling. Tested across eight models, the dose matters: capable models want the full set, weaker ones do best with a compact core plus per-task retrieval, and saturated models gain nothing. Notably, gpt-oss-120b jumped +16.1pp on task completion for just +5% tokens. The lesson: agentic memory is a calibrated dosage, not a default feature.