It's Friday evening at a busy airport. Delays have stacked up, crews are stretched and costs have climbed. The operational data is in one place; the rules are in another.

In this free live masterclass, we build one AI agent with Gemini that reads both. The agent queries airport data through SQL, retrieves relevant operating rules from policy documents, and answers: what went wrong on Friday, what did it cost, and which rules were broken? We'll inspect its evidence and citations, and test where its answers need checking.

We'll cover:

- Framing the airport operations problem

- Connecting an agent to data and policy documents

- Producing answers with evidence and citations

- Testing the agent and handling missing information

- Live Q&A

We'll share the session notebook so you can adapt the pattern to your own domain.

Hosted by Mohammad Arshad, founder of Decoding Data Science.

All airport data and rules used in the demo are synthetic and created for teaching.