用Oracle数据库构建企业级长时智能体记忆系统，支持跨会话状态持久化。
Oracle Agent Memory as an Enterprise Memory Substrate for Long-Horizon AI Agents
- 基于Oracle数据库构建分层记忆架构，实现状态生命周期管理。
- 长任务准确率达93.8%，相比基线减少10.7倍的令牌使用量。
- 适合需要长期记忆与高可靠性的企业AI系统部署场景。
智能体记忆是长时程智能体的核心系统问题。实际部署需在长时间对话中保留任务状态，跨会话恢复用户特定事实与偏好，并积累过往结果的程序性知识。这超越了文档检索范畴：记忆层需决定哪些交互成为持久状态，状态的范围如何界定，在延迟约束下如何检索，以及如何随时间修订或删除。本报告研究基于Oracle数据库构建的Oracle Agent Memory作为原生数据库记忆底座。讨论围绕三大主题展开：记忆的全生命周期管理（摄入、提取、整合、检索、摘要、修订或移除）；分层架构将活跃记忆核心与被动存储接口分离，支持用户、智能体和线程级别的显式作用域控制；评估方法融合下游任务准确率与记忆中心指标，如证据检索、召回率、延迟及估算令牌消耗。报告总结LongMemEval结果，准确率达93.8%，对比扁平历史基线节省约10.7倍令牌，与可用外部基线进行比较，并附有面向实施的附录材料，涵盖设置、线程生命周期与搜索语义。
原文摘要 · Abstract (English)
Agent memory is a systems problem for long-horizon agents. Practical deployments require retention of task state across extended conversations, recovery of user-specific facts and preferences across sessions, and accumulation of procedural knowledge from prior outcomes. These requirements extend beyond document retrieval: a memory layer must determine which interactions become durable state, how that state is scoped, how it is retrieved under latency constraints, and how it is revised or removed over time. This report studies Oracle Agent Memory as a database-native memory substrate built on Oracle Database. Three themes organize the discussion: memory as a lifecycle spanning ingestion, extraction, consolidation, retrieval, summarization, and revision or removal; a layered architecture that separates an active memory core from a passive memory-store interface with explicit scope control across users, agents, and threads; and evaluation methodology in which downstream task accuracy is complemented by memory-centric measures such as evidence retrieval, recall, latency, and estimated token use. The report summarizes LongMemEval results, reaching 93.8% accuracy, compares Oracle Agent Memory against flat-history baselines, using about 10.7x fewer tokens, and published or reported external baselines where available, and closes with implementation-oriented appendix material covering setup, thread lifecycle, and search semantics.
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