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Agentic Context Management: Memory and Cost as Architecture Problems
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A July 23, 2026 arXiv paper proposes Agentic Context Management (ACM), a framework for managing the information held in production AI agents’ reasoning contexts. The authors argue that agent failures often stem from overloaded conversation histories, prompts, tool definitions and tool outputs rather than deficient reasoning. Accumulating this material can cause missed recalls and token costs that rise with every turn. ACM treats context as a lifecycle rather than solely a storage-and-retrieval problem. Its five primitives are architecting, ingesting, scoping, anticipating, and compacting and consolidation. The framework covers deciding what information to retain, structuring it, selecting stores for different data types, preserving provenance while consolidating or forgetting information, retrieving relevant material, anticipating future needs, and fitting context within a token budget. It is designed to operate across organizational scope hierarchies rather than only for individual users. The paper argues that naive context accumulation produces token costs that grow quadratically with conversation length. It says crude summarization can reduce costs linearly but risks an accuracy cliff, while validated compaction can preserve fidelity with linear cost growth. The described multi-tenant reference service, Maximem Synap, reported 92% on LongMemEval and 93.2% on LoCoMo under the paper’s Section 6 configuration.