Build with AI: Production-Ready AI Agent Systems on Databricks
1h 28mIntermediate2026-05-26
Authors
Viktoria Semaan
Course details
Building AI agents is becoming more accessible, but moving from a working prototype to production requires knowing when multiagent architectures make sense, how to evaluate their performance, and which tools can help you build more reliably.
In this course, join Viktoria Semaan, Principal AI Evangelist at Databricks, as she demonstrates how to design and deploy a complete multiagent system in Databricks using a realistic bookstore scenario. Explore multiagent architecture patterns, evaluate agent performance with MLflow 3, and apply Databricks tools to accelerate development. Along the way, examine each system component in detail, troubleshoot common integration challenges, and explore reusable architecture patterns alongside working code from the course repository that you can adapt to your own unique use cases.
In this course, join Viktoria Semaan, Principal AI Evangelist at Databricks, as she demonstrates how to design and deploy a complete multiagent system in Databricks using a realistic bookstore scenario. Explore multiagent architecture patterns, evaluate agent performance with MLflow 3, and apply Databricks tools to accelerate development. Along the way, examine each system component in detail, troubleshoot common integration challenges, and explore reusable architecture patterns alongside working code from the course repository that you can adapt to your own unique use cases.
Skills covered
DatabricksAI Agents and Agentic SystemsBuilding with AIData EngineeringData AnalysisData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOne-Off
Concepts
Introduction
- Building enterprise AI agents.
- What this course covers
Data Foundations
- AI functions for parsing data
- Building the products table
Vector Seach
- Understanding vector search
- Creating and testing a vector index
Custom RAG Agent
- RAG concepts and playground prototyping
- Building custom RAG agent in notebook
- MLflow and evaluations
- Custom scorers and deployment
AI Genie
- AI BI Genie - Generate tables
- AI BI Genie - Setting up the space
Knowledge Assistant (No-Code RAG)
- Creating a Knowledge Assistant
Evaluating Knowledge Assistants
- Improving Knowledge Assistant with human feedback
MCP Servers
- Adding external MCP (web search)
Supervisor Agents
- Building and testing supervisor agent
Conclusion
- Troubleshooting and clean-up