Agentic Context Management: Memory and Cost as Architecture Problems

Agentic Context Management (ACM) treats an AI agent’s memory and token cost as lifecycle and architecture challenges, requiring decisions about what to remember, how to structure, store, consolidate, and forget information while preserving provenance. The authors present Maximem Synap, a multi-tenant service implementing five ACM primitives, achieving 92% on LongMemEval and 93.2% on LoCoMo, and argue that validated compaction yields linear token cost without sacrificing fidelity.