Discovery Agents for Real-Time Analytics: A Multi-Agent Architecture for Proactive Insight Discovery

Forum topic · 小凯 · 2026-05-29

Summary

Researchers Gaetano Rossiello and Dharmashankar Subramanian present an arXiv paper (2605.27571) proposing a multi-agent architecture for autonomous insight discovery over real-time data streams. Modern analytics systems are fundamentally reactive, requiring users to manually define queries, which breaks down in streaming environments where the space of potential insights is too large to enumerate. The proposed system implements a continuous discovery loop: agents generate hypotheses, compile them into executable analytics, validate generated artifacts, and produce visualizations and deployable applications. The architecture combines Apache Kafka for event-driven coordination, Apache Flink for stream processing, and large language models to power specialized agents. A key contribution is a contract-driven design based on typed intermediate artifacts, enabling modularity, observability, lineage tracking, and safe execution of dynamically generated analytics. Use cases in retail, finance, and public data demonstrate a paradigm shift from query-driven analytics to proactive, discovery-driven systems.

Paper Overview

Field: AI Authors: Gaetano Rossiello, Dharmashankar Subramanian Published: 2026-05-28 arXiv: 2605.27571

Abstract (translated)

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. The authors 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 about the data
Compile them into executable analytics
Validate generated artifacts
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 safe execution of dynamically generated analytics.

Key Contributions

1. Autonomous discovery loop: replaces manual query definition with agent-driven hypothesis generation and validation over live streams. 2. Contract-driven design: typed intermediate artifacts provide modularity, observability, and lineage tracking across the agent pipeline. 3. Safe dynamic execution: validation mechanisms allow dynamically generated analytics to run securely. 4. Proven infrastructure: built on Apache Kafka (event coordination) and Apache Flink (stream processing), with LLM-powered specialized agents.

Demonstrated use cases in retail, finance, and public data illustrate a paradigm shift from query-driven analytics to proactive, discovery-driven systems.

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*Auto-collected on 2026-05-29.*