Deploy and govern enterprise agentic AI solutions on Azure

  • Learning Path
  • 4 Modules

At a glance

Deploying and governing enterprise agentic AI solutions on Azure requires a structured approach to CI/CD pipelines, zero-trust security, responsible AI compliance, and agent lifecycle management. This learning path guides you through the operational and governance capabilities required to run multi-agent systems reliably and safely at enterprise scale.

Prerequisites

  • Completion of AI-103 "Develop AI agents on Azure" and AI-300 "Operationalize generative AI applications" (or equivalent)
  • Familiarity with Microsoft Foundry project deployment and Azure managed identities
  • Working experience with GitHub Actions for CI/CD pipeline design
  • Basic knowledge of Azure security services (Azure Key Vault, Microsoft Entra ID)
  • Familiarity with responsible AI principles, Azure AI Content Safety guardrails, and basic Microsoft Entra ID concepts (managed identities, RBAC)
  • Python programming proficiency.

Modules in this learning path

Implement CI/CD pipelines for multi-agent systems with GitHub Actions

Implement enterprise CI/CD pipelines for multi-agent systems using GitHub Actions and Microsoft Foundry. Design coordinated deployment pipelines with agent version compatibility validation, implement progressive deployment strategies including canary releases, configure multi-environment agent deployments with infrastructure as code, and automate rollback triggered by quality regression detection.

Secure multi-agent systems with Azure zero-trust architecture

Secure production multi-agent systems using Azure zero-trust architecture principles. Apply per-agent managed identities with least-privilege access and design authentication flows covering managed identity, on-behalf-of (OBO), user-delegated, and key-based patterns. Manage secrets lifecycle with Azure Key Vault, including rotation and customer-managed keys (CMK) encryption, and design network controls to prevent lateral movement. Implement multitenant data isolation and configure compliance controls for enterprise regulatory requirements.

Scale responsible AI governance with Azure AI Content Safety and Microsoft Foundry

Scale responsible AI governance to enterprise multi-agent systems using Azure AI Content Safety and Microsoft Foundry. Design systematic fairness and bias monitoring pipelines for agent decision chains, implement transparency and explainability mechanisms for complex multi-agent outputs, configure privacy protection for multi-agent data pipelines, and establish audit trail frameworks for enterprise accountability.

Govern the enterprise agent lifecycle in Microsoft Foundry

Govern the enterprise agent lifecycle in Microsoft Foundry. Design agent versioning strategies and approval workflows for controlled production updates, implement usage quotas and cost allocation models for agent operations, establish change management and behavioral risk assessment processes, and design agent retirement workflows that minimize disruption to downstream consumers.

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