Agentic Context Management Solving Agent Memory And Cost

---
name: agentic-context-management-solving-agent-memory-and-cost
description: 'Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems'
metadata:
  {
    "arxiv_id": "2607.21503",
    "utility": 1.0,
    "date_added": "2026-07-26"
  }
---

# Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems

arXiv: 2607.21503  
Published: 2026-07-23  
Utility: 1.0

## Summary
Production AI agents' failures are less often due to an inability to reason well and more often because they cannot manage what is in their reasoning context: conversation histories, large prompts, large tool definitions, and ballooning tool outputs. Agents drown in their own accumulating history while paying a token cost that grows every turn, producing missing recalls within and across conversations. The incumbent response treats this as a storage-and-retrieval problem. We argue that framing is too narrow. Actively managing what an agent holds in mind is a lifecycle, not merely a store: it spans deciding what to remember, extracting and structuring it, choosing the right store per data type, consolidating and forgetting while preserving provenance, deciding what is relevant now, anticipating what is needed next, and compacting context to a budget without losing what matters. In serious production this operates not over a single user but across an organizational scope hierarchy. We name this discipline Agentic Context Management (ACM) and decompose it into five primitives: architecting, ingesting, scoping, anticipating, and compacting & consolidation. We then make the economic case: naive context accumulation grows token cost quadratically in conversation length, crude summarization buys linear cost at the price of an accuracy cliff, and only validated compaction achieves linear cost with preserved fidelity. We describe a reference implementation, Maximem Synap, that realize...

## Key Information
- **Title**: Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
- **Authors**: [Extract from entry]
- **Primary Category**: cs.AI

## Potential Skill Application
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## Reference
- arXiv: https://arxiv.org/abs/2607.21503