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

View PDF HTML (experimental) Abstract:Production AI agents' failures are less often due to an inability to reason well and more often because they cannot manage what is in their reasoning context: conversation histories, large prompts, large tool definitions, and ballooning tool outputs. Agents drown in their own accumulating history while paying a token cost that grows every turn, producing missing recalls within and across conversations. The incumbent response treats this as a storage-and-retr...

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