Design and implement multi-agent AI solutions

Outline

• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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