Thinking of ACE? We Can Do It with Fewer Tokens

IBM researchers compare Agentic Context Engineering (ACE) and ALTK-Evolve, two systems that help language-model agents learn reusable lessons from past task trajectories without retraining or human labels. Both retain detailed, counted lessons rather than compressing them into short summaries, but ACE continuously injects a complete playbook into every inference step, while ALTK-Evolve consolidates guidelines and adjusts delivery through a small core, task-specific retrieval, or the full set depending on model capacity. In controlled AppWorld tests using the same ReAct agent and base models, the authors report that ALTK-Evolve achieved higher task and scenario completion on DeepSeek-V3.2 while using 263,000 tokens per task versus ACE’s 634,000, and roughly matched or slightly exceeded ACE on gpt-oss-120b using 116,000 tokens versus 777,000. The analysis suggests comprehensive guidance can aid easier tasks or stronger models, whereas selective retrieval may better support difficult tasks and weaker models by reducing distracting context, though results were single-run evaluations and the compared systems used different prompt templates for their no-memory baselines.

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