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Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
This paper introduces ACE (Agentic Context Engineering), a framework that treats LLM contexts as evolving playbooks to overcome limitations like brevity bias and context collapse in context adaptation. ACE employs a modular process of generation, reflection, and curation with structured, incremental updates. It significantly outperforms strong baselines on agent and domain-specific benchmarks, achieving self-improvement without labeled supervision and reducing adaptation costs. ✨
Article Points:
1
ACE treats contexts as evolving playbooks, accumulating and refining strategies over time.
2
ACE prevents context collapse and brevity bias through structured, incremental updates.
3
The framework uses a modular workflow: Generator, Reflector, and Curator for adaptation.
4
ACE achieves average gains of +10.6% on agents and +8.6% on domain-specific benchmarks.
5
It adapts effectively without labeled supervision, leveraging natural execution feedback.
6
ACE reduces adaptation latency by 86.9% and lowers rollout and token costs significantly.
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Problem Addressed
Brevity Bias
Context Collapse
Framework
Evolving Playbooks
Modular Workflow
- Generator
- Reflector
- Curator
Key Innovations
Incremental Delta Updates
Grow-and-Refine Mechanism
Dedicated Reflector
Performance Gains
Agents +10.6%
Domain-Specific +8.6%
Matches IBM-CUGA
Efficiency
Lower Latency -86.9%
Fewer Rollouts
Lower Token Cost
Implications