InfraAdvisor AI
A reference architecture for building, training, deploying, and monitoring an AI agent end-to-end on Azure — a multi-agent advisor for bridges, disasters, energy, water, and federal procurement data.
InfraAdvisor AI is a reference architecture for AEC/O&M consulting — architecture, engineering, construction, and operations & maintenance firms — that answers questions about bridges, disasters, energy grids, water systems, and federal procurement using live U.S. government data instead of a static knowledge base.
Nine Airflow DAGs pull and normalize government data into Azure AI Search as a hybrid vector + keyword index. A consultant’s question goes through a router LLM that picks one of five domain specialists, each of which calls a scoped set of MCP tools (11 total) against real government APIs before an LLM synthesizes a cited answer. The whole pipeline — ingestion, the agent, and the React UI — is instrumented with Datadog across APM, LLM Observability, RUM, Data Streams, Data Jobs, and CSPM, since a reference architecture that only demonstrates the AI part and skips the operational half isn’t actually a complete reference.
A single generalist agent with every tool available makes worse decisions than five narrow specialists that each see only their own domain's tools.
The interesting failures in an AI agent aren't always in the LLM call — a stale Airflow ingestion job or a bad embedding batch upstream can degrade answers just as badly.