How Much Memory Does Your Agent Actually Need?

In our previous post , we compared ALTK-Evolve with ACE and showed that how you deliver an agent's self-distilled guidelines — a few retrieved per task vs. the whole set injected — drives both accuracy and cost. This post steps back to the question that comes before it: how much should you give it?

Equipping an agent with agentic memory sounds simple: distill lessons from its past work, put them back in context, and more experience should mean better performance. It doesn't always work that way. When we scaled the evaluation to eight models — from a 30B dense model to frontier proprietary systems — one finding stood out:

Agentic memory is not a feature you switch on. It's a dose you calibrate to the model.

What puts a model into one pattern rather than another isn't simply parameter count. Benchmark headroom, context-window size, architecture, guideline quality, and task distribution all appear to shape where a model lands, and separating those factors is ongoing work. The practical takeaway holds either way: the right dose of memory depends on the model, and we can calibrate it.

No model weights are updated. The learning loop changes the guidance available to the agent , not the underlying model — which is exactly why it's cheap to adopt and portable across the eight models we tested.

Beyond AppWorld. These results are validated on AppWorld — a rigorous multi-step benchmark, but a single one. Broader agent benchmarks and real-world deployments are in progress.

ALTK-Evolve lets an agent learn from its own past trajectories: distilling reusable guidelines and injecting them back at inference time, with no weight updates and no human annotation.

The right dose differs by model tier: strong models with headroom want the full guideline set, weaker models do best with a compact core plus per-task retrieval, and saturated models show no measurable gain.

Curated retrieval can be both the most accurate and the cheapest option: gpt-oss-120b gained +16.1pp task completion at only +5% tokens — and prompt caching keeps even the full guideline set affordable in production.