Reimagining Enterprise RAG for Multi-Agent Systems: A Knowledge Layer That Organizes Before It Retrieves· 企业多智能体系统的逆向工程知识层
Enterprises are deploying agentic AI across the software development lifecycle, and they need the behavioral logic of the systems they act on, not stakeholder documentation. A seven-agent reverse-engineering pipeline recovered this logic, processing 241 repositories across 18 services into thousands of artifacts describing scope, journeys, rules and acceptance criteria. Recovery does not confer usability. Entities like offer, eligibility, and activation recur across services under a shared vocabulary, related in name but operationally distinct, so one end-to-end journey implicates over a thousand near-identical rules that must not be conflated. Flat vector retrieval fails, returning an identical rule from the wrong service at high similarity with no signal of error. Content-level ontology,
提出一种企业级多智能体逆向工程知识层，提升系统逻辑恢复的准确性和可用性。
- 核心方法
- 构建了一个七智能体的逆向工程管道，该管道能够处理大量代码仓库，生成描述系统范围、用户旅程、规则和验收标准的数千个工件。通过内容级别的本体论组织知识，解决跨服务规则混淆的问题。
- 适合谁读
- 研究者、工程师、产品经理
- 要解决的问题
- 企业在部署多智能体系统时，需要准确恢复系统的逻辑，而非仅仅获取利益相关者的文档；传统的向量检索方法在跨服务知识恢复时存在混淆问题。
- 关键实验
- 处理了 241 个代码仓库，涵盖 18 个服务，生成了数千个描述系统逻辑的工件，验证了方法的有效性。
- 主要贡献
- 提出了内容级别的本体论方法，有效组织和区分跨服务的知识，提高了企业多智能体系统中系统逻辑的可用性和准确性。
- 意义与局限
- 本研究为企业部署多智能体系统提供了更有效的逻辑恢复工具，但其复杂性可能限制了在小型企业的应用。