Discovery Agents for Real-Time Analytics: Toward Proactive Insight Systems
This paper presents a multi-agent architecture for autonomous insight discovery over real-time data streams. It uses Apache Kafka, Flink, and large language models to continuously generate, validate, and visualize hypotheses, shifting from reactive query-driven analytics to proactive discovery-driven systems.
Article intelligence
Key points
- Proposes multi-agent architecture for autonomous discovery of insights in real-time streams.
- Integrates Kafka, Flink, and LLMs for hypothesis generation, validation, and visualization.
- Uses contract-driven design with typed intermediate artifacts for modularity and safety.
- Demonstrates proactive analytics in retail, finance, and public data use cases.
Why it matters
This matters because proposes multi-agent architecture for autonomous discovery of insights in real-time streams.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.27571] Discovery Agents for Real-Time Analytics: Toward Proactive Insight Systems
[Submitted on 26 May 2026]
Title:Discovery Agents for Real-Time Analytics: Toward Proactive Insight Systems
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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 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
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Gaetano Rossiello [view email] [v1] Tue, 26 May 2026 18:43:25 UTC (1,752 KB)
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