用多智能体自动发现实时数据中的潜在洞察，变被动查询为主动预警。
Discovery Agents for Real-Time Analytics: Toward Proactive Insight Systems
- 构建多智能体系统，通过生成假设、执行分析、验证结果形成闭环发现流程。
- 基于类型化中间产物的合约设计，支持动态分析的安全执行与可追溯性。
- 适用于零售、金融等场景，让系统从响应查询转向主动发现关键信息。
现代分析系统本质上是被动响应的，需用户在日益复杂且持续演化的数据上定义查询。在实时流处理环境中，这种范式失效，因潜在洞察空间过大，无法人工枚举。本文提出一种多智能体架构，实现对实时数据流的自主洞察发现。系统采用持续发现循环：智能体生成假设，将其编译为可执行分析，验证产出物，并生成可视化和可部署应用。架构利用 Apache Kafka 实现事件驱动协调，Apache Flink 进行流处理，大语言模型驱动专用智能体。核心贡献是基于类型化中间产物的合约式设计，支持模块化、可观测性、数据溯源及动态分析的安全执行。通过零售、金融和公共数据等多个应用场景验证，该架构推动分析系统从查询驱动转向主动发现驱动。
原文摘要 · Abstract (English)
Modern analytics systems are fundamentally reactive, requiring users to define queries over increasingly complex and continuously evolving data. In real-time streaming environments, this paradigm breaks down, as the space of potential insights becomes too large to enumerate manually. We present a multi-agent architecture for autonomous insight discovery over real-time data streams. The system implements a continuous discovery loop in which agents generate hypotheses, compile them into executable analytics, validate generated artifacts, and produce visualizations and deployable applications. The architecture leverages Apache Kafka for event-driven coordination, Apache Flink for stream processing, and large language models to implement specialized agents. A key contribution is a contract-driven design based on typed intermediate artifacts, enabling modularity, observability, lineage, and safer execution of dynamically generated analytics. Through use cases in retail, finance, and public data, we show how this architecture supports a shift from query-driven analytics to proactive, discovery-driven systems.
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