From Prompt to Production: How to use AI Code Assistants for Python Data Systems
Code-generating LLMs have matured to the point where they can reliably scaffold data pipelines and data agents, when used in a supervised, engineering-first workflow. This tutorial demonstrates how to combine modern AI coding assistants with a production-ready Python deployment platform (Tower.dev) to build and operate real data systems.
Participants will learn how to structure collaborative Human/AI Assistant development loops, where engineers provide architecture, domain knowledge, and review, while AI accelerates implementation. We will build a data pipeline and a lightweight data agent, iterating with an AI assistant to generate, test, and improve code.
The session also covers critical operational concerns such as:
- Security
- Scaling
- Observability
- Debugging
You will also see how production feedback can be looped back into the assistant to continuously improve generated code.
This is not about “vibe coding” a website. It is about disciplined, review-driven AI collaboration that meaningfully improves productivity for data practitioners at all levels.
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.
Serhii Sokolenko is a co-founder of Tower, a Pythonic platform for data flows and agents running on top of open analytical storage. Prior to founding Tower, Serhii worked at Databricks, Snowflake and Google on data processing and databases.