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Discovery Agents for Real-Time Analytics: Toward Proactive Insight Systems

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arXiv:2605.27571v1 Announce Type: new Abstract: 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 g

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Computer Science > Artificial Intelligence [Submitted on 26 May 2026] Discovery Agents for Real-Time Analytics: Toward Proactive Insight Systems Gaetano Rossiello, Dharmashankar Subramanian 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. Comments: Accepted at Supporting Our AI Overlords (SAO) at the ACM Conference on AI and Agentic Systems (CAIS), May 26 2026, San Jose, CS, USA Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Databases (cs.DB) Cite as: arXiv:2605.27571 [cs.AI] (or arXiv:2605.27571v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2605.27571 Focus to learn more Submission history From: Gaetano Rossiello [view email] [v1] Tue, 26 May 2026 18:43:25 UTC (1,752 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev | next > new | recent | 2026-05 Change to browse by: cs cs.CL cs.DB References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

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