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

A new paper argues that many AI agent failures stem from poor context handling, not weak reasoning.

It proposes Agentic Context Management, a lifecycle framework with five primitives to decide what to keep, organize, scope, anticipate, and compact.

The paper includes a reference system and reports strong benchmark results on memory-heavy tasks.

If adopted, the approach could make production agents cheaper, more reliable, and better at retaining useful information over long interactions.