AI Agents Drown in Their Own Context: A New Framework Cuts Token Costs
Agentic Context Management: Memory and Cost as Architecture Problems
Production AI agents often fail not because they reason poorly, but because they can't manage what's in their context: growing histories, large prompts, and bloated tool outputs. This paper argues that treating this as a storage problem is too narrow. Instead, it introduces Agentic Context Management (ACM), a discipline with five primitives—architecting, ingesting, scoping, anticipating, and compacting & consolidation. The authors show that naive context accumulation leads to quadratic token costs, while validated compaction achieves linear costs with preserved fidelity. They present Maximem Synap, a reference implementation that scores 92% on LongMemEval and 93.2% on LoCoMo, and discuss benchmarks' blind spots like latency and context-rot.
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.