Paper
PowerDAG: Reliable Agentic AI System for Automating Distribution Grid Analysis
Authors
Emmanuel O. Badmus, Amritanshu Pandey
Abstract
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.17418v1</id>\n <title>PowerDAG: Reliable Agentic AI System for Automating Distribution Grid Analysis</title>\n <updated>2026-03-18T06:52:47Z</updated>\n <link href='https://arxiv.org/abs/2603.17418v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.17418v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='eess.SY'/>\n <published>2026-03-18T06:52:47Z</published>\n <arxiv:primary_category term='eess.SY'/>\n <author>\n <name>Emmanuel O. Badmus</name>\n </author>\n <author>\n <name>Amritanshu Pandey</name>\n </author>\n </entry>"
}