Thinking of ACE? We Can Do It with Fewer Tokens

ALTK-Evolve and ACE both let an agent learn from its own trajectories. The difference is what they do with what they learn — and that decides the token bill.

Give an LLM agent a realistic multi-step task — split a bill, find a song, reconcile an order across nine simulated apps — and when it fails, it usually isn't for lack of knowledge. It mis-paginates an API, resolves the wrong person, or returns a value when none was asked for. The model knows the APIs; what it hasn't internalized is how to use them reliably. That's learnable from the agent's own history.

Two recent systems do exactly this, on the same kind of agent: ACE (Agentic Context Engineering) and our ALTK-Evolve (introduced here). Both are a form of agentic memory — turning an agent's past trajectories into reusable lessons and feeding them back at inference time, no weight updates, no human labels. They even agree on the hard part. Where they part ways is delivery.

A note on words, because the two systems name things differently: we'll call the raw thing an agent learns a lesson. ACE organizes its lessons into one comprehensive, evolving playbook; we consolidate ours into individually retrievable guidelines. Same lessons, two containers.

What we agree on

Both systems refuse to compress.

ACE names the failure modes precisely: brevity bias — optimization collapsing toward short, generic instructions — and context collapse — a model asked to rewrite its whole context each step summarizing the detail away. Its answer is to keep a rich, itemized playbook, with a helpful/harmful counter on every bullet, and let the model distill relevance at read time.

We reach the same conclusion from the other direction. Every distinct guideline keeps a support count — how many independent episodes produced it — and we never summarize the store down to a handful of rules. A lesson five different tasks discovered is a different object from one that appeared once, and both are worth keeping.