Addressing Agent Memory and Costs as Lifecycle and Architectural Issues

Failures in production AI agents are often due to their inability to manage the information in their reasoning context rather than a lack of reasoning capability. Agents struggle with accumulating conversation histories and large prompts, leading to increasing costs with each interaction. This paper argues that this issue should be viewed as a lifecycle problem rather than a mere storage and retrieval challenge. The proposed discipline, Agentic Context Management (ACM), breaks down into five core primitives: architecting, ingesting, scoping, anticipating, and compacting & consolidation.