This 90-minute hands-on tutorial shows how to design, build, and deploy Python data pipelines and data agents using AI coding assistants in a supervised engineering workflow.

Outline

• The state of AI code generation for data engineering
• Designing collaborative Human/LLM development loops
• Building a data pipeline with structured AI assistance
• Creating a simple data agent
• Deploying and operating Python workloads in production using Tower.dev
• Using logs, observability, and runtime feedback to guide AI-driven refactoring
• Best practices, risks, and guardrails

Participants will leave with practical patterns for integrating AI into real-world data engineering workflows, from prototype to production.