From Tools to Teammates: Engineering AI Agents for Data & Analytics
About this session
AI agents become much more interesting when they move beyond answering questions and begin participating in real analytical work.
This session follows one analytic from idea to client to show how agents can participate across the full data and analytics lifecycle: exploring research and methodology, evaluating whether data is fit for purpose, translating methodology into tested production code, investigating production issues, and delivering governed analytics directly into user workflows.
Rather than building one “super-agent,” we’ll explore an architecture based on specialized agents and skills with explicit roles, handoffs, and human decision points. Using a real Morningstar implementation, we’ll show how agentic capabilities can combine governed enterprise data, semantic context, MCP-enabled tools, methodology, and evaluation to produce reliable analytical outcomes.
We’ll also examine where autonomy should stop. Agents can investigate, recommend, and execute bounded tasks—but critical decisions such as ambiguous entity resolution, methodology interpretation, or production remediation may still require human approval.
The goal is not to demonstrate that AI can write code or call tools. It is to show what has to be engineered around an agent before it can become a trustworthy participant in a production analytics system.
Speakers
Key takeaways
- Design specialized AI agents across the analytics lifecycle rather than relying on a single general-purpose agent
- Ground agents in governed enterprise data, semantic context, methodologies, tools, and entitlements
- Use agents for analytical investigation and production troubleshooting, not just conversational interfaces or code generation.