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

• URL: http://arxiv.org/abs/2605.27571v1
• Date: Tue, 26 May 2026 18:43:25 GMT
• Title: Discovery Agents for Real-Time Analytics: Toward Proactive Insight Systems
• Abstract summary: We present a multi-agent architecture for autonomous insight discovery over real-time data streams.<n>The architecture implements a continuous discovery loop in which agents generate hypotheses, compile them into executable analytics, validate generated artifacts, and produce visualizations and deployable applications.<n>A key contribution is a contract-driven design based on typed intermediate artifacts, enabling modularity, observability, lineage, and safer execution of dynamically generated analytics.
• Score: 10.456625151583701
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics [45.48758200829718]
  We introduce QwenPaw-Data, an agentic data system designed for enterprise intelligent data analysis.<n>QwenPaw-Data consolidates heterogeneous assets into reusable, governable, and evolvable analysis assets, then turns natural-language requests into end-to-end analytical spanning data understanding, retrieval, analysis, report generation, and decision support.
  arXiv  Detail & Related papers  (2026-07-13T02:38:00Z)
• VESTA: Visual Exploration with Statistical Tool Agents [56.76643750765825]
  We introduce VESTA: Visual Exploration with Statistical Tool Agents.<n>We show that VESTA's dynamic tool creation outperforms prior agentic pipelines.<n>We also introduce DAWN, a benchmark targeting distribution fitting and time series.
  arXiv  Detail & Related papers  (2026-05-29T21:57:37Z)
• Agentic Insight Generation in VSM Simulations [4.626393563516877]
  We propose a decoupled, two-step agentic architecture for value stream map simulations.<n>By separating orchestration from data analysis, the system leverages progressive data discovery infused with domain expert knowledge.<n>Results from multiple state-of-the-art large language models demonstrate the framework's viability.
  arXiv  Detail & Related papers  (2026-04-14T08:11:53Z)
• A Hierarchical Multi-Agent System for Autonomous Discovery in Geoscientific Data Archives [0.0]
  PANGAEA-GPT is a hierarchical multi-agent framework designed for autonomous data discovery and analysis.<n>Unlike standard Large Language Model (LLM) wrappers, our architecture implements a centralized Supervisor-Worker topology.<n>We demonstrate the system's capacity to execute complex, multi-step deterministic runtime with minimal human intervention.
  arXiv  Detail & Related papers  (2026-02-24T20:37:38Z)
• DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories [52.57197752244638]
  We introduce DeepImageSearch, a novel agentic paradigm that reformulates image retrieval as an autonomous exploration task.<n>Models must plan and perform multi-step reasoning over raw visual histories to locate targets based on implicit contextual cues.<n>We construct DISBench, a challenging benchmark built on interconnected visual data.
  arXiv  Detail & Related papers  (2026-02-11T12:51:10Z)
• AgentSkiller: Scaling Generalist Agent Intelligence through Semantically Integrated Cross-Domain Data Synthesis [30.512393568258105]
  Large Language Model agents demonstrate potential in solving real-world problems via tools, yet generalist intelligence is bottlenecked by scarce high-quality, long-horizon data.<n>We propose AgentSkiller, a fully automated framework synthesizing multi-turn interaction data across realistic, semantically linked domains.
  arXiv  Detail & Related papers  (2026-02-10T03:21:42Z)
• AgentLongBench: A Controllable Long Benchmark For Long-Contexts Agents via Environment Rollouts [78.33143446024485]
  We introduce textbfAgentLongBench, which evaluates agents through simulated environment rollouts based on Lateral Thinking Puzzles.<n>This framework generates rigorous interaction trajectories across knowledge-intensive and knowledge-free scenarios.
  arXiv  Detail & Related papers  (2026-01-28T16:05:44Z)
• Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding [61.36285696607487]
  Document understanding is critical for applications from financial analysis to scientific discovery.<n>Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs) face key limitations.<n>Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG.
  arXiv  Detail & Related papers  (2025-10-17T02:33:16Z)
• LLM Agents for Interactive Workflow Provenance: Reference Architecture and Evaluation Methodology [3.470217255779291]
  We introduce an evaluation methodology, reference architecture, and open-source implementation that leverages interactive Large Language Model (LLM) agents for runtime data analysis.<n>Our approach uses a lightweight, metadata-driven design that translates natural language into structured provenance queries.<n> Evaluations across LLaMA, GPT, Gemini, and Claude, covering diverse query classes and a real-world chemistry workflow, show that modular design, prompt tuning, and Retrieval-Augmented Generation (RAG) enable accurate and insightful agent responses.
  arXiv  Detail & Related papers  (2025-09-17T13:51:29Z)
• Deep Research Agents: A Systematic Examination And Roadmap [109.53237992384872]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• Research on the Application of Spark Streaming Real-Time Data Analysis System and large language model Intelligent Agents [1.4582633500696451]
  This study explores the integration of Agent AI with LangGraph to enhance real-time data analysis systems in big data environments.<n>The proposed framework overcomes limitations of static, inefficient stateful computations, and lack of human intervention.<n>System architecture incorporates Apache Spark Streaming, Kafka, and LangGraph to create a high-performance sentiment analysis system.
  arXiv  Detail & Related papers  (2024-12-10T05:51:11Z)
• Building Interpretable and Reliable Open Information Retriever for New Domains Overnight [67.03842581848299]
  Information retrieval is a critical component for many down-stream tasks such as open-domain question answering (QA) We propose an information retrieval pipeline that uses entity/event linking model and query decomposition model to focus more accurately on different information units of the query. We show that, while being more interpretable and reliable, our proposed pipeline significantly improves passage coverages and denotation accuracies across five IR and QA benchmarks.
  arXiv  Detail & Related papers  (2023-08-09T07:47:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.