Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
Score
78首次系统性地将Agent上下文管理定义为生命周期并给出五原语，填补了现有研究以存储-检索窄框架的空白。
理论框架清晰，经济模型有说服力，实验数据支持充分，但部分细节（如压缩算法）未完全公开。
直接指导生产级Agent的上下文成本优化，五原语可转化为具体工程实践，参考实现具有落地参考。
当前Agent部署爆发期，上下文失控正是行业痛点，论文响应及时。
来源arXiv顶级会议论文，作者与机构可信，实验设计规范。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper presents a clear and practical conceptual framework with a tiered solution, offering significant reference value for agent engineering.
AI commentary
「As an editor, I believe this paper hits the core pain point of current AI agent deployment—runaway context costs. For any team building production agents, the five ACM primitives offer a systematic thinking framework and practical guidance. The benchmark results of Maximem Synap further validate its effectiveness. It's a must-read for all agent developers.」
Quick overview
Production AI agents often fail not due to poor reasoning but inability to manage conversation history, large prompts, tool definitions, and outputs. This paper proposes Agentic Context Management (ACM), decomposing it into five primitives: architecting, ingesting, scoping, anticipating, and compacting & consolidation. It argues that only validated compaction achieves linear token cost with preserved fidelity. Reference implementation Maximem Synap achieves 92% on LongMemEval and 93.2% on LoCoMo. The paper also discusses underexplored dimensions like latency, token efficiency, and context-rot resistance.
- Proposes Agentic Context Management (ACM) paradigm, treating context management as a lifecycle problem rather than a storage-and-retrieval problem.
- Defines five primitives: architecting, ingesting, scoping, anticipating, and compacting & consolidation.
- Economic analysis: naive accumulation yields quadratic token cost, crude summarization buys linear cost at accuracy cliff, only validated compaction achieves linear cost with fidelity.
- Reference implementation Maximem Synap achieves 92% on LongMemEval and 93.2% on LoCoMo, setting new SOTA.
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