# Thinking of ACE? We Can Do It with Fewer Tokens

DevFeed: [Thinking of ACE? We Can Do It with Fewer Tokens](<https://devfeed.tech/articles/thinking-of-ace-we-can-do-it-with-fewer-tokens-7263.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/ibm-research/altk-evolve-sldd>)

Author: Vatche Isahagian; Jayaram Radhakrishnan; Vinod Muthusamy; Gaodan Fang; Punleuk Oum; G Thomas; Ashwath Vaithinathan Aravindan; Evelyn Duesterwald; Merve Unuvar

Published: 2026-08-11T13:37:10Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [API](<https://devfeed.tech/topics/api.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [api](<https://devfeed.tech/tags/api.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llm](<https://devfeed.tech/tags/llm.md>), [memory](<https://devfeed.tech/tags/memory.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

## AI overview

This article compares ACE and ALTK-Evolve, two systems that turn an LLM agent's past task trajectories into reusable lessons for inference without weight updates or human labels. It explains their shared rejection of aggressive memory compression and contrasts ACE's evolving playbook with ALTK-Evolve's individually retrievable guidelines, including differences in consolidation and delivery that affect token usage.

## Source excerpt

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.