Edge-Based Agentic Retrieval-Augmented Generation for Autonomous FHWA Bridge Inspection Compliance

BridgeGuard is an air‑gapped, agentic Retrieval‑Augmented Generation system designed to automate compliance verification for the Federal Highway Administration’s bridge inspection requirements. It combines vector search over the FHWA Recording and Coding Guide with structured SQL queries against National Bridge Inventory data, guided by a stateful multi‑step ReAct planning loop that runs locally on commodity edge hardware. The system achieves near‑perfect classification and citation accuracy on large bridge inventories while operating without external network access, and ablation studies show that both vector search and the agentic loop are essential for correct compliance reasoning.

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• rag
• agents
• benchmarks
• data-engineering