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cs.AI updates on arXiv.org

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GraphMind: From Operational Traces to Self-Evolving Workflow Automation
Yiwen Zhu, J · 2026-05-19 · via cs.AI updates on arXiv.org

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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.
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.17617 [cs.AI]
  (or arXiv:2605.17617v2 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2605.17617

arXiv-issued DOI via DataCite

Submission history

From: Yiwen Zhu [view email]
[v1] Sun, 17 May 2026 19:22:22 UTC (708 KB)
[v2] Tue, 26 May 2026 00:01:30 UTC (610 KB)

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