Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
- Published
- Source
- arXiv
- Paper number
- 714
- Field
- AI / General
- arXiv ID
- 2607.21503
Key points
- It redefines agent context management as a 5-stage lifecycle, design, extraction, scope, prediction, and compression, instead of treating it as simple storage and retrieval.
- It proves mathematically that unlimited context accumulation increases token cost as O(n²), while naive summarization causes sharp drops in accuracy.
- Its scope-aware design, which respects organizational layers such as user, customer, and client, simultaneously supports privacy isolation and organizational knowledge accumulation.
- The reference implementation, Maximem Synap, achieves 92 percent on LongMemEval and 93.2 percent on LoCoMo.
- It contrasts itself with commercial memory systems such as Mem0, Zep, and Cognee, which mostly focus on extraction and storage and do not cover the full 5-stage lifecycle.
Paper links
External research summaries. These are not HDATF publications or measured product results.