Resolving complex, multi-system incidents at scale is no longer a job for static scripts or basic chat assistants. It calls for autonomous agents that reason about a problem, act on live systems safely, and adapt as conditions change.
You start by building a goal-oriented reasoning loop that turns an unstructured alert into ordered diagnostic steps. You then expose external diagnostics as secure, typed tools over the Model Context Protocol, kept inside non-root, read-only sandboxes.
Next you orchestrate specialised agents that hand work off cleanly and share a persistent memory of past incidents. Finally you make the system production-ready: you instrument it with Azure Monitor and Application Insights, measure Task Adherence and Groundedness in Azure AI Foundry, and defend it with content safety filters and human approval for risky actions.
This advanced, build-first course is for technical individual contributors such as AI developers and support engineers, and for technical leaders such as operations managers and automation strategists, who want to expand into agentic AI. You finish with a portfolio-ready capstone, the Contoso Self-Healing Incident Response System.
