Read September 2026
Agentic Context Management
My takeaway
Problem
These days AI Agents fail in their performance less often because of reasoning, it’s scarce knowledge but more often because of its non-optimal context management system.
Solution
This paper introduces a Agent Context Management (ACM) lifecycle. They say ACM is no more ‘nice-to-have’ thing, it's a basic need for AI Agents to perform better with optimised context. Memory is not just a store which is used to do CRUDe operations, this lifecycle they proposed has 5 blocks: architecting → ingesting → scoping → anticipating → compacting & consolidation. Architecting: this involves defining the shape of memory, basically the datatype of each module’s module-specific memory to have, but currently the system uses shared data type for modules. Ingesting: this involves ingesting the context into memory in a structured way with information rich data, not just appending all the information. Scoping: this involves defining a boundary for each user level to avoid user A’s context being passed onto user B. anticipating: this defines (just like its literal meaning) to prepare context even before an explicit request arrives by another agent. Compacting & Consolidation: this defines compacting the whole context (to reduce token cost and improve retrieval accurate, faster) without removing/modifying the crucial information based on context. Also they made few evaluations on these blocks which we can see in the results section.
- AgentMemory
- LLMMemory
- ContextManagement