Production AI agents fail more often from poor context management than reasoning flaws. They drown in accumulating conversation histories, large prompts, and tool outputs while facing growing token costs. Current approaches treat this as a storage-and-retrieval problem, but we argue it's a lifecycle challenge.

We introduce Agentic Context Management (ACM) as a discipline spanning deciding what to remember, extracting relevance, choosing appropriate stores, and compacting context while preserving fidelity. ACM decomposes into five primitives: architecting, ingesting, scoping, anticipating, and compacting & consolidation. Economically, naive accumulation grows costs quadratically, crude summarization achieves linear cost with accuracy loss, while validated compaction delivers linear cost with preserved fidelity.

Our reference implementation, Maximem Synap, realizes these primitives as a multi-tenant service, achieving 92% on Long Mem Eval and 93.2% on Lo Co Mo. We address organizational scope hierarchies and propose new benchmark dimensions including latency, token efficiency, and context-rot resistance.