Design and implement multi-agent AI solutions (AI-500)

Architect Production-Grade Multi-Agent AI Solutions in Azure

Design Stateful Agentic Loops with Microsoft Foundry Agent Service

• Examine production agentic loop architecture
• Examine the Foundry Responses API and Agents v2 model
• Implement agent reflection and planning cycles
• Design session state and context management
• Implement fork-based sessions and conversation resumption
• Migrate stateful agentic loops from Agents v1 to Agents v2

Implement Advanced Multi-Agent Orchestration Patterns in Microsoft Foundry

• Differentiate agentic AI from multi-agent AI architectures
• Examine advanced orchestration architectures
• Implement hub-and-spoke orchestration
• Design parallel agent spawning and synchronisation
• Compare orchestration frameworks

Apply Task Decomposition and Agent Collaboration Strategies in Microsoft Foundry

• Design prompt chaining workflows
• Implement dynamic adaptive task decomposition
• Design agent handoff message schemas
• Ensure handoff reliability and context preservation
• Optimise decomposition granularity

Design Enterprise-Scale Agent Communication with A2A in Azure

• Design A2A agent ecosystems at scale
• Implement distributed shared state management
• Design context isolation and sharing strategies
• Build conflict detection and resolution mechanisms
• Resolve conflicts and maintain audit trails

Build Production-Grade Multi-Agent Capabilities with Microsoft Foundry

Design Advanced Prompting Strategies for Production AI Agents

• Design multiturn reasoning prompt architectures
• Implement prompt injection defenses
• Build system prompt frameworks for agent control
• Design multi-intervention guardrail architectures
• Implement prompt versioning and optimisation
• Automate prompt regression and optimisation
• Design fine-tuning strategy and data pipelines

Build Enterprise-Grade Tool Ecosystems with MCP and Microsoft Foundry

• Design production MCP server architecture
• Build MCP servers with error handling and fallback
• Implement tool selection and routing logic
• Govern tool dependencies and versioning

Implement Advanced RAG Pipelines with Azure AI Search and Microsoft Foundry

• Design hybrid search architectures
• Implement reranking and context ranking
• Design dynamic knowledge source routing
• Optimise chunking and embedding strategies

Design Multi-Agent Memory Architectures with Azure Cosmos DB

• Examine memory architecture patterns
• Implement semantic memory with vector storage
• Optimise memory retrieval and context injection
• Configure context window optimisation
• Design memory retention and consolidation
• Enforce memory privacy and audit compliance

Deploy and Govern Enterprise Agentic AI Solutions on Azure

Implement CI/CD Pipelines for Multi-Agent Systems with GitHub Actions

• Design multi-agent deployment pipelines
• Implement progressive deployment strategies
• Configure multi-environment agent deployment strategies
• Automate rollback procedures

Secure Multi-Agent Systems with Azure Zero-Trust Architecture

• Apply zero-trust identity to agent networks
• Secure agent access with JIT and workload identity
• Design authentication flows and secrets lifecycle
• Prevent lateral movement in agent networks
• Implement tenant context propagation and data isolation
• Validate tenant boundaries and enforce encryption
• Configure compliance controls for regulated agent deployments

Scale Responsible AI Governance with Azure AI Content Safety and Microsoft Foundry

• Design fairness and bias monitoring
• Implement transparency and explainability
• Configure privacy protection in multi-agent workflows
• Establish audit and accountability frameworks

Govern the Enterprise Agent Lifecycle in Microsoft Foundry

• Design agent versioning and approval workflows
• Implement usage quotas and rate limiting
• Design cost allocation and chargeback models
• Establish agent retirement and deprecation processes

Monitor, Evaluate, and Operate Multi-Agent AI Solutions in Azure

Implement Distributed Observability for Multi-Agent Solutions with OpenTelemetry

• Design distributed tracing for multi-agent solutions
• Implement structured logging for agent decisions
• Configure telemetry aggregation and dashboards
• Build anomaly detection for agent behavior

Design Evaluation Frameworks for Multi-Agent Solutions with Microsoft Foundry

• Define multi-agent success metrics
• Implement LLM-as-judge evaluation for multi-agent systems
• Design synthetic test datasets for multi-agent evaluation
• Build regression testing pipelines to detect agent drift

Optimise Multi-Agent Performance and Cost in Microsoft Foundry

• Design model routing for agent ecosystems
• Implement multi-level caching strategies
• Optimise token usage and context management
• Balance quality, cost, and latency tradeoffs

Design Human-in-the-Loop Approval Workflows with Power Automate and Microsoft Teams

• Design confidence-based escalation for human intervention
• Implement approval workflows for agent-initiated actions
• Build active learning from human feedback
• Configure audit workflows for regulated decisions

Debug and Respond to Production Multi-Agent Incidents in Azure

• Implement agent replay for production debugging
• Design root cause analysis for agent failures
• Configure automated incident detection and remediation
• Establish incident response and post-mortem processes