AI-500T00-A: Design and implement multi-agent AI solutions

Design Logical Architectures for Multi-Agent AI Systems

Decompose Complex Business Requirements into Agents, Tasks and Workflows

Define Agent Roles, Responsibilities and Autonomy Levels

Design Stateful Agentic Loops and Control Flows

Implement Human-in-the-Loop (HITL) Workflows

Design Agent-to-Agent Communication and Collaboration Strategies

Select Appropriate Models for Specialized Agent Tasks

Design Short-Term and Long-Term Agent Memory Architectures

Implement Shared Context and State Management

Configure Agent Identity, Authentication and Permission Boundaries

Design Scalable and Secure Multi-Agent Infrastructure

Apply Responsible AI and Zero Trust Architecture Principles

Plan Distributed Observability, Logging and Monitoring

Select Development Tools and Environments for Multi-Agent Solutions

Design Evaluation Frameworks for Multi-Agent AI Systems

Implement Human Review and Feedback Mechanisms

Evaluate Agent Memory, Tool Usage, Knowledge Retrieval and Prompts

Configure Automated Testing and Quality Evaluation

Implement LLM-as-a-Judge Evaluation Frameworks

Generate Synthetic Test Data for Multi-Agent Workflows

Monitor Behavioral Drift and Quality Regression

Optimize Agent Coordination and Task Execution

Analyze Context Window Issues and Memory Reliability

Optimize Token Consumption and Model Usage

Configure Prompt, Semantic and Response Caching

Implement Distributed Tracing with OpenTelemetry

Monitor Multi-Agent Workflows Using Azure Monitor

Configure Logging, Metrics and Alerting in Microsoft Foundry

Monitor Agent Health, Latency, Reliability and Performance

Analyze AI Resource Consumption, Quotas and Operational Costs

Troubleshoot Agent Failures and Workflow Execution Issues

Create and Configure Microsoft Foundry Resources and Projects

Implement Identity and Access Management with Managed Identities

Configure Role-Based Access Control (RBAC)

Secure Generative AI Infrastructure Using Private Networking

Deploy Resources Using Bicep Templates and Azure CLI

Deploy Foundation Models for Production Workloads

Configure Serverless API Endpoints and Managed Compute

Select Appropriate Models for Enterprise AI Applications

Implement Model Versioning and Deployment Strategies

Configure Provisioned Throughput for High-Volume Workloads

Design and Manage Prompt Templates and Variants

Implement Prompt Versioning Using Git Repositories

Compare and Evaluate Prompt Performance

Design Security Controls for Multi-Agent AI Systems

Implement Microsoft Entra ID and Role-Based Access Control (RBAC)

Configure Agent Identities and Least-Privilege Access

Implement OAuth 2.0 and Secure Authentication Flows

Manage Secrets, Keys and Certificates with Azure Key Vault

Apply Zero Trust Security Principles to AI Agent Architectures

Implement Responsible AI and Content Safety Guardrails

Configure Input, Tool and Output Validation

Design Custom Guardrails for Enterprise AI Applications

Implement AI Red Teaming and Security Testing

Configure Human Oversight and Approval Controls

Implement Agent Lifecycle Governance and Version Management

Configure CI/CD Pipelines for Multi-Agent AI Solutions

Manage Infrastructure as Code (IaC) Deployments

Implement Development, Testing and Production Environments

Configure Blue/Green and Canary Deployment Strategies

Implement Automated Testing and Regression Validation

Design Release Management, Rollback and Recovery Processes