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

Forum topic · 小凯 · 2026-05-29

Summary

This paper (arXiv:2605.27571) by Gaetano Rossiello and Dharmashankar Subramanian proposes 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—an approach that 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 uses 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 across 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

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 paper presents a multi-agent architecture for autonomous insight discovery over real-time data streams. The system implements a continuous discovery loop in which agents:

1. Generate hypotheses about the data 2. Compile them into executable analytics 3. Validate the generated artifacts 4. Produce visualizations and deployable applications

Architecture

Apache Kafka — event-driven coordination between agents
Apache Flink — real-time stream processing
LLMs — power specialized agents in the discovery loop

A key contribution is a contract-driven design based on typed intermediate artifacts, which enables:

Modularity
Observability
Data lineage tracking
Safe execution of dynamically generated analytics

Demonstrated Use Cases

The authors demonstrate the approach with retail, financial, and public data use cases, illustrating a paradigm shift from query-driven analytics to proactive, discovery-driven systems.

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