For any enterprise service organization, the answers to critical questions are buried in a chaotic archipelago of disparate data sources: CRM databases, ticketing systems, application logs, and more. The real challenge isn't analysis; it's the painstaking, manual process of fetching, joining, and making sense of this scattered data.
This talk demystifies this process by introducing a two-stage, multi-agentic workflow that automates data engineering and interpretation.

Stage 1: The Data Forage. We'll show how an Orchestrator Agent breaks down a high-level business question (e.g., "Analyze ticket trends for our top clients") into sub-tasks.

Stage 2: The Sense-Making Layer. Raw data is just noise. The second, and most crucial, stage involves agents designed to understand the business. We'll explore how a Business Context Agent takes the raw, structured data from the forage and applies organizational knowledge. It resolves ambiguities, understands what defines a "top client" based on database fields, maps customer_id from one system to org_id in another, and ultimately transforms the raw data into a coherent, analysis-ready dataset.