About This Architecture

Agentic AI-driven incident management on Azure orchestrates detection, classification, remediation, and ticketing through a serverless event-driven pipeline. Datadog monitoring triggers alerts via Event Grid to Logic Apps, which queues incidents for an AI Agent Orchestrator running on Azure Container Instances, coordinating with Azure OpenAI for severity classification and Azure ML Rules Engine for decision logic. The system automates ticket lifecycle management in Ivanti, executes remediation actions, sends notifications to Teams, and maintains audit trails in Cosmos DB and Log Analytics. This architecture demonstrates zero-trust patterns using Managed Identity and Key Vault, enabling security teams to respond to major incidents in minutes rather than hours. Fork this diagram on Diagrams.so to customize subnets, add additional AI models, or integrate alternative ITSM platforms. The modular design supports scaling from single-region deployments to multi-region failover with minimal changes.

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How can I build an AI-powered incident management system on Azure that automatically detects, classifies, and remediates incidents?

This diagram shows a complete agentic AI incident pipeline: Datadog alerts trigger Event Grid, which routes to Logic Apps and a Service Bus queue feeding an AI Agent Orchestrator on ACI. The orchestrator uses Azure OpenAI for severity classification and Azure ML Rules Engine for decision logic, then coordinates remediation agents, Ivanti ticket creation/updates, and Teams notifications—all secured