Treating agent context as a lifecycle and architecture problem, not just storage

Agentic Context Management: Memory and Cost as Architecture Problems

The paper proposes Agentic Context Management (ACM), breaking agent context handling into five primitives: architecting, ingesting, scoping, anticipating, and compacting & consolidation. The core argument: production agents fail less from poor reasoning and more from ballooning context—naive accumulation drives token cost up quadratically with conversation length, while crude summarization trades linear cost for an accuracy cliff. A reference implementation, Maximem Synap, hits 92% on LongMemEval and 93.2% on LoCoMo. The authors note existing benchmarks miss latency, token efficiency, and context-rot resistance. The post doesn't disclose specific latency figures or deployment scale.

Why it matters: Reframes agent context management as a lifecycle problem, closer to engineering reality than typical benchmark papers. Hits all three HKR axes, but the paper is a framework proposal without large-scale production validation, so it stays at 78, the featured threshold.