PowerDAG: Reliable Agentic AI System for Automating Distribution Grid Analysis

TL;DR

PowerDAG is a reliable agentic AI system designed to automate complex distribution grid analysis, employing adaptive retrieval and JIT supervision to enhance performance and robustness.

Contribution

The paper introduces PowerDAG, a novel agentic AI system with adaptive retrieval and JIT supervision, significantly improving reliability in complex distribution grid analysis tasks.

Findings

PowerDAG achieves 100% success with GPT-5.2 on unseen queries.

PowerDAG outperforms baseline systems by 6-50 percentage points.

PowerDAG maintains high success rates with smaller open-source models.

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: adaptive retrieval, which uses a similarity-decay cutoff algorithm to dynamically select the most relevant annotated exemplars as context, and 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.