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

Agenda

1 - 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
• Module assessment

2 - 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 synchronization
• Compare orchestration frameworks
• Module assessment

3 - 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
• Optimize decomposition granularity
• Module assessment

4 - 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
• Module assessment

5 - 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 optimization
• Automate prompt regression and optimization
• Design fine-tuning strategy and data pipelines
• Module assessment

6 - 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
• Module assessment

7 - 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
• Optimize chunking and embedding strategies
• Module assessment

8 - Design multi-agent memory architectures with Azure Cosmos DB

• Examine memory architecture patterns
• Implement semantic memory with vector storage
• Optimize memory retrieval and context injection
• Configure context window optimization
• Design memory retention and consolidation
• Enforce memory privacy and audit compliance
• Module assessment

9 - 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
• Module assessment

10 - 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
• Module assessment

11 - 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
• Module assessment

12 - 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
• Module assessment

13 - 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
• Module assessment

14 - 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
• Module assessment

15 - Optimize multi-agent performance and cost in Microsoft Foundry

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

16 - 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
• Module assessment

17 - 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
• Module assessment