Reliable agents must manage what enters context, what stays there, and what gets safely removed.

Storing everything is not enough because agents also need rules for relevance, access, timing, structure, and compression.

The paper proposes Agentic Context Management, a 5-part system for deciding what an AI agent should keep, retrieve, share, prepare, and safely compress.

Production agents often fail because old messages, prompts, tool descriptions, and tool outputs crowd the model's limited working context.

Agentic Context Management groups 5 linked jobs: architecting, ingesting, scoping, anticipating, and compacting with consolidation.

Maximem Synap turns raw activity into organised facts, enforces context boundaries, follows linked evidence, prepares likely information early, and checks compressed context for losses.

In its reported setup, Synap scored 92% on LongMemEval and 93.2% on LoCoMo categories 1 to 4, although multi-session reasoning remained weakest.

Sending all prior context makes total token use grow with conversation length squared, while crude summaries can erase facts and validated compaction keeps growth roughly linear.

- arxiv. org/abs/2607.21503

Title: "Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems"