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Governing AI Agents
Beginner
1 hour 30 mins
Topics
Agents
Congratulations on making it this far. Let's recap what you've learned in this course. For governance in action, we have our four pillars that we implemented with the use of Unity Catalog and MLflow. Those pillars were Lifecycle Management, Risk Management, Security, and Observability. For our Lifecycle Management, we created Version-controlled UC functions and views. We created MLflow model registry for agent versioning and rollback capabilities. This is a foundation for a CI/CD pipeline integration. And for our risk management, we have data classification enforced at every layer. We have a legal department filtering for compliance, and we have group and service principal explicitly defined and the permissions granted. For security, we have a multi-layer defense, so our maskings, our views, our groups, and our functions all working together. We implement the principle of least privilege, and we have no direct table access for agents. We have everything clearly defined and again, we have that principle of least privilege, so we aren't giving the agent access to anything more than is necessary for its task for HR analytics. Observability, so we have UC function calls logged. We have MLflow traces available, and we have a full audit trail from query to data. So now that we have all these capabilities built, what are our next steps to really build production level agents? So taking your governance to that enterprise scale. Well, for Lifecycle Management, there's still Blue-green deployments for agents, Canary releases with gradual rollout, A/B testing frameworks for agent performance. For Risk Management, there's Multi-model routing and fallbacks. There's automated rollback on performance degradation. There's additional monitoring and alerting, monitoring and alerting. For security, using something like an AI Gateway for centralized endpoint management, using rate limiting and usage tracking on all those endpoints, and having enhanced authentication and compliance logging. And then for observability, real-time performance dashboards, cost tracking and budget alerts, as well as anomaly detection for agent behavior. This also goes into our monitors on the evaluation metrics that we've used. Alright, so going through our checklist now for our pillars, looking at Lifecycle Management, did we accomplish a separation of duties? Can you safely promote changes through environments with proper review and rollback capabilities? We're checking yes for that. Risk Management, did we best apply defense in depth? Do we have multiple layers of protection to catch issues before they impact production? Yes, going to check that box. Security, this is our best practice for Least Privilege Access. Are all your data sources accessible only to authorized agents and users? Going to check yes to this. And then lastly, Observability. Can we audit everything? Can you trace every tool your agent used, when it ran, and what data it accessed and what it returned? I'm going to check yes to this as well. I want to thank everyone for joining me throughout this course, and I'm excited to see what you build.
