Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
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-retrieval problem. We argue that framing is too narrow. Actively managing what an agent holds in mind is a lifecycle, not merely a store: it s
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“Possibly related via embedding similarity 0.64 (not asserted). Timestamp check: artifact slightly before paper (-21d).”
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- FuzzySimilar title/name (fuzzy) · 87%zhayujie/CowAgent →
“Fuzzy title match (0.94): “Agentic Context Management: Solving Agent Memory and Cost by” ≈ “zhayujie/CowAgent””
- FuzzySimilar title/name (fuzzy) · 66%TencentCloud/TencentDB-Agent-Memory →
“Fuzzy title match (0.78): “Agentic Context Management: Solving Agent Memory and Cost by” ≈ “TencentCloud/TencentDB-Agent-Memory””
- FuzzySimilar title/name (fuzzy) · 59%NousResearch/hermes-agent →
“Fuzzy title match (0.73): “Agentic Context Management: Solving Agent Memory and Cost by” ≈ “NousResearch/hermes-agent””