GraphMind: From Operational Traces to Self-Evolving Workflow Automation
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,...
| Authors | Yiwen Zhu, Joyce Cahoon, Anna Pavlenko et al. |
|---|---|
| Primary category | cs.AI |
| Published | 17 May 2026 |
| Last updated at source | 26 May 2026 |
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