Most AI agents pass their security and compliance review once — on demo day — and then quietly drift out of compliance in real use. The reason is simple: teams check the agent by hand, at a single moment in time. The moment passes, the agent keeps changing, and no one is watching.

This session shows a better approach: instead of treating the audit as a one-time event, you make it continuous, so the agent constantly checks and corrects itself — the way a live dashboard shows real-time status instead of a once-a-year report.
We'll build this step by step on Microsoft Azure.

You'll get a simple five-point scorecard every enterprise agent should be measured against continuously — effectiveness, security, cost, reliability, and compliance — and you'll see "agents that check other agents": a small set of specialized AI reviewers, each grading one part of the scorecard. Then we'll add automatic gates in the delivery pipeline using Microsoft Foundry and GitHub, so a risky change is caught before it ever reaches production. Finally, we'll watch a live incident handled end to end: Azure SRE Agent resolves the issue, and Work IQ pulls the surrounding context — who owned it, who approved the change — so the audit trail is complete.

Everything maps to Microsoft's Cloud Adoption Framework, the official best-practice methodology for running on Azure. You'll leave able to turn a one-time audit into an always-on dashboard.

What you'll learn (takeaways):

- Why a security and compliance audit should be continuous, not a one-time check
- A reusable five-point scorecard for any production AI agent
- How to build the build → check → fix → operate loop on Azure using Foundry, GitHub, Azure SRE Agent, and Work IQ

Session format / length: Breakout session. Primary 45 min; also delivered as 30 min and 15 min lightning

Tags / topics: Azure, AI Agents, Microsoft Foundry, GitHub, Azure SRE Agent, Work IQ, Cloud Adoption Framework, DevOps, Security & Compliance, Responsible AI

Target audience: AI startup founders, solution architects, DevOps/platform engineers, and innovation or governance leaders building production AI agents on Azure