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Course Overview
This course focuses on the practical skills needed to architect and develop multi-agent AI solutions using Microsoft Foundry and Azure, validating your ability to design logical architecture for multi-agent solutions, build and integrate tool ecosystems, implement multi-agent orchestration and integration of monitoring, security and governance.
Who Should Attend
Candidates for this course, are expert-level practitioners who have subject matter expertise in designing, building, and optimizing scalable, production-ready, multi-agent AI systems, solutions and workflows. Out of your job role, you work closely with developers, machine learning engineers, platform engineers, data scientists, and business stakeholders to translate complex requirements into production-ready, multi-agent solutions.
Course Outline
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