A new agent memory framework says context management is an architecture problem

Gaurav Dadhich · hf · 2026-07-27

This paper argues that production agent failures are usually caused less by weak reasoning than by poor context management: chat histories, large prompts, tool specs, and tool outputs keep accumulating until the agent drowns in its own memory and cost.

The authors frame this as a lifecycle and architecture problem, not just storage/retrieval, and propose Agentic Context Management (ACM) with five primitives: architecting, ingesting, scoping, anticipating, and compacting/consolidation. They also make an economic case: naive accumulation leads to quadratic token cost, summarization creates an accuracy cliff, and only validated compaction can keep cost linear while preserving fidelity.

A reference implementation, Maximem Synap, is described as a multi-tenant service and reports 92% on LongMemEval and 93.2% on LoCoMo under the paper’s configuration. The paper ends by noting missing benchmark dimensions such as latency, token efficiency, and resistance to context rot.

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A new agent memory framework says context management is an architecture problem

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