The paper introduces Agentic Context Management (ACM) as a framework to address the failure modes of production AI agents, which often stem from inefficient management of conversation histories, tool definitions, and expanding tool outputs. Rather than treating memory as a simple storage-and-retrieval issue, the authors propose viewing context as a lifecycle problem. ACM is decomposed into five fundamental primitives: architecting, ingesting, scoping, anticipating, and compacting & consolidation. The research highlights the economic inefficiencies of current methods, noting that naive context accumulation leads to quadratic token cost growth, while crude summarization results in an "accuracy cliff." In contrast, the authors argue that validated compaction enables linear cost scaling while maintaining high fidelity. To demonstrate this approach, the authors present Maximem Synap, a multi-tenant reference implementation that realizes these five primitives. Experimental results indicate that this architecture achieves high performance on specialized benchmarks, reporting 92% on LongMemEval and 93.2% on LoCoMo. The authors conclude by proposing new evaluation dimensions essential for production environments, including latency, token efficiency, and resistance to context-rot, while suggesting a future direction toward organizational-level context management.