GraphMind: From Operational Traces to Self-Evolving Workflow Automation

Yiwen Zhu; Joyce Cahoon; Anna Pavlenko; Qiushi Bai; Nima Shahbazi; Divya Vermareddy; Meina Wang; Mathieu Demarne; Swati Bararia; Wenjing Wang; Hemkesh Vijaya Kumar; Hannah Lerner; Katherine Lin; Steve Toscano; Miso Cilimdzic; Subru Krishnan

arXiv · 2605.17617

GraphMind: From Operational Traces to Self-Evolving Workflow Automation

Yiwen Zhu·Joyce Cahoon·Anna Pavlenko·Qiushi Bai·Nima Shahbazi·Divya Vermareddy·Meina Wang·Mathieu Demarne·Swati Bararia·Wenjing Wang·Hemkesh Vijaya Kumar·Hannah Lerner·Katherine Lin·Steve Toscano·Miso Cilimdzic·Subru Krishnan

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Abstract

Complex operational workflows coordinating personnel, tools, and information are central to system operations, yet end-to-end automation remains challenging due to extensive human input requirements and limited ability to adapt over time. We present GraphMind, a system that constructs, executes, and evolves action-centric workflow graphs with minimal human effort. The system operates in three phases. First, a scalable offline pipeline extracts structured workflow graphs from large volumes of human resolution traces, capturing problems, actions, and their causal relationships. Second, an online multi-agent traversal engine navigates the graph to dynamically construct and execute workflows, combining graph-guided retrieval with LLM-driven reasoning at each step. Third, Adaptive Traversal Reinforcement (ATR) reinforces successful traversal paths, enabling execution-informed graph adaptation. GraphMind has been deployed across four production cloud database services for incident investigation. Evaluated on 93 held-out incidents and validated via blind expert review, the system outperforms an Agentic Summary-RAG baseline in mitigation reach, hallucination rate, and diagnostic throughput while requiring 8x less retrieval context. The ATR layer reduces hallucination rate by 26%, demonstrating that workflow graphs can learn from execution feedback. A 12-week field study confirms practical value: 97% of scored conversations yield actionable results within interactive latency.

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Yiwen Zhu, Joyce Cahoon, Anna Pavlenko, Qiushi Bai, Nima Shahbazi, Divya Vermareddy, Meina Wang, Mathieu Demarne, Swati Bararia, Wenjing Wang, Hemkesh Vijaya Kumar, Hannah Lerner, Katherine Lin, Steve Toscano, Miso Cilimdzic, Subru Krishnan. 2026-05-17. GraphMind: From Operational Traces to Self-Evolving Workflow Automation. https://arxiv.org/abs/2605.17617

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