Emmanuel O. Badmus

Emmanuel O. Badmus, Amritanshu Pandey

This paper introduces PowerDAG, an agentic AI system for automating complex distribution-grid analysis. We address the reliability challenges of state-of-the-art agentic systems in automating complex engineering workflows by introducing two innovative active mechanisms: (i) \textbf{adaptive retrieval}, which uses a similarity-decay cutoff algorithm to dynamically select the most relevant annotated exemplars as context, and (ii) \textbf{just-in-time (JIT) supervision}, which actively intercepts and corrects tool-usage violations during execution. On a benchmark of unseen distribution grid analysis queries, PowerDAG achieves a 100\% success rate with GPT-5.2 and 94.4--96.7\% with smaller open-source models, outperforming base ReAct (41--88\%), LangChain (30--90\%), and CrewAI (9--41\%) baselines by margins of 6--50 percentage points.

Emmanuel O. Badmus, Peng Sang, Dimitrios Stamoulis et al.

Rapid electrification and decarbonization are increasing the complexity of distribution grid (DG) operation and planning, necessitating advanced computational analyses to ensure reliability and resilience. These analyses depend on disparate workflows comprising complex models, function calls, and data pipelines that require substantial expert knowledge and remain difficult to automate. Workforce and budget constraints further limit utilities' ability to apply such analyses at scale. To address this gap, we build an agentic system PowerChain, which is capable of autonomously performing complex grid analyses. Existing agentic AI systems are typically developed in a bottom-up manner with customized context for predefined analysis tasks; therefore, they do not generalize to tasks that the agent has never seen. In comparison, to generalize to unseen DG analysis tasks, PowerChain dynamically generates structured context by leveraging supervisory signals from self-contained power systems tools (e.g., GridLAB-D) and an optimized set of expert-annotated and verified reasoning trajectories. For complex DG tasks defined in natural language, empirical results on real utility data demonstrate that PowerChain achieves up to a 144/% improvement in performance over baselines.