About Course
In this course, you’ll learn how to integrate governance into every stage of your agent’s lifecycle, from defining access control to monitoring runtime behavior.
Explore what it means to govern an agent, how to apply governance policies to a real dataset in Databricks, and how to add observability to track and debug performance. Through this course, you’ll know how to build agents that handle data responsibly while maintaining visibility, and safety.
Skills you will build:
- Apply the four pillars of agent governance (lifecycle management, risk management, security, and observability) to build safer, production-ready agents.
- Use Unity Catalog, Databricks’ centralized governance layer, to organize data, manage permissions, and enforce least-privilege data access for your agents.
- Manage data permissions for Databricks identities and assign your agent an identity with appropriate access.
- Apply governance to an agent analyzing an HR dataset: create anonymized views, mask personal information, and build tools that provide only the data needed.
- Build, evaluate, and prepare your agent for production using MLflow to log, version, and deploy it with proper governance.
- Deploy your governed agent with a secure, traceable endpoint in Databricks.
By applying these governance practices to your own agents, you’ll build observable systems that handle data securely!
