Integrate Data Governance Into Your Agent's Workflow in This New Course!

Why It Matters

As organizations increasingly permit agents to access and modify sensitive data, embedding governance and observability into agent workflows reduces legal, security, and reputational risk while improving reliability and auditability of AI-driven processes.

Summary

Databricks and instructor Amber Robbins launch a course, "Governing AI Agents," that teaches practitioners how to integrate data governance into the lifecycle of autonomous agents. The course covers practical steps—least-privilege data access, masking sensitive fields, guardrails for personal information, and observability—to prevent data leakage or accidental modification. Students build an analyst agent using MLflow and the OpenAI SDK, apply tracing and custom evaluation metrics, and deploy the agent on Databricks while monitoring performance and failures. The curriculum emphasizes end-to-end visibility into what data agents access and how they process it.

Original Description

Learn more: https://bit.ly/4nh0gZ5

Introducing Governing AI Agents, a short course built in collaboration with Databricks and taught by Amber Roberts.

As AI agents autonomously access larger and more sensitive data, governance becomes essential. Without proper controls, an agent can accidentally expose personal information, modify sensitive records, or operate beyond its intended scope. As a developer, you need to design agents that are not only capable but also safe, compliant, and observable in production.

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. You’ll 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.

By the end, you’ll know how to build agents that handle data responsibly while maintaining visibility, and safety.

What you’ll do:

- 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!

Enroll now: https://bit.ly/4nh0gZ5