Construction companies generate massive amounts of operational and financial data across ERP systems, field reports, inspection logs, and project controls tools. Yet most organizations still rely on dashboards and manual investigation to understand project performance.
This session demonstrates how agentic workflows built with Databricks Agent Bricks transform construction data into automated operational intelligence. Using a synthetic construction services scenario, we show how multiple AI agents analyze project financials, field inspections, and operational data stored in the lakehouse.
Specialized agents interpret structured ERP data and unstructured inspection documents, while a supervisor agent orchestrates investigations across multiple sources to detect cost variance, identify recurring defects, and recommend operational actions. The demo illustrates how the Databricks Data Intelligence Platform enables governed, explainable automation while preserving enterprise data governance.
Attendees will learn practical design patterns for building agent-driven systems that move construction analytics beyond dashboards toward actionable intelligence.

This session will be the first public presentation of this demo. The demo uses synthetic construction industry data and does not contain any client or confidential information.

The session includes a live architecture walkthrough and demonstration built on the Databricks Data Intelligence Platform using Agent Bricks, Unity Catalog governance, and lakehouse data patterns.

Target Audience:
Data engineers, data architects, AI engineers, platform engineers, and analytics leaders interested in building enterprise agentic AI systems on governed data platforms.

The session focuses on architecture patterns, governance considerations, and practical design approaches for building multi-agent workflows using enterprise data.