{"slug": "agentic-context-management-memory-and-cost-as-architecture-problems", "title": "Agentic Context Management: Memory and Cost as Architecture Problems", "summary": "A new arXiv paper argues that production AI agents fail primarily due to poor context management rather than reasoning limitations, proposing a discipline called Agentic Context Management (ACM) with five primitives: architecting, ingesting, scoping, anticipating, and compacting & consolidation. The authors report that naive context accumulation grows token cost quadratically with conversation length, while their reference implementation, Maximem Synap, achieves 92% on LongMemEval and 93.2% on LoCoMo, and they call for benchmarks to capture latency, token efficiency, and context-rot resistance.", "body_md": "# Computer Science > Artificial Intelligence\n\n[Submitted on 23 Jul 2026]\n\n# Title:Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems\n\n[View PDF](/pdf/2607.21503)\n\n[HTML (experimental)](https://arxiv.org/html/2607.21503v1)\n\nAbstract: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 realizes the five primitives as a multi-tenant service and reports 92% on LongMemEval and 93.2% on LoCoMo under the configuration detailed in Section 6. We close with dimensions existing benchmarks do not yet capture, latency, token efficiency, and context-rot resistance, and the frontier of decision-level and organization-level context the category points toward.\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/agentic-context-management-memory-and-cost-as-architecture-problems", "canonical_source": "https://arxiv.org/abs/2607.21503", "published_at": "2026-08-26 02:35:25+00:00", "updated_at": "2026-08-26 02:43:41.034060+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-research", "ai-infrastructure", "large-language-models"], "entities": ["arXiv", "Maximem Synap", "LongMemEval", "LoCoMo"], "alternates": {"html": "https://wpnews.pro/news/agentic-context-management-memory-and-cost-as-architecture-problems", "markdown": "https://wpnews.pro/news/agentic-context-management-memory-and-cost-as-architecture-problems.md", "text": "https://wpnews.pro/news/agentic-context-management-memory-and-cost-as-architecture-problems.txt", "jsonld": "https://wpnews.pro/news/agentic-context-management-memory-and-cost-as-architecture-problems.jsonld"}}