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

- Design stateful agentic loops with Microsoft Foundry agent serviceExamine production agentic loop architectureExamine the Foundry responses API and agents v2 modelImplement agent reflection and planning cyclesDesign session state and context managementImplement fork-based sessions and conversation resumptionMigrate stateful agentic loops from agents v1 to agents v2Module assessment- Implement advanced multi-agent orchestration patterns in Microsoft FoundryDifferentiate agentic AI from multi-agent AI architecturesExamine advanced orchestration architecturesImplement hub-and-spoke orchestrationDesign parallel agent spawning and synchronizationCompare orchestration frameworksModule assessment- Apply task decomposition and agent collaboration strategies in Microsoft FoundryDesign prompt chaining workflowsImplement dynamic adaptive task decompositionDesign agent handoff message schemasEnsure handoff reliability and context preservationOptimize decomposition granularityModule assessment- Design enterprise-scale agent communication with A2A in AzureDesign A2A agent ecosystems at scaleImplement distributed shared state managementDesign context isolation and sharing strategiesBuild conflict detection and resolution mechanismsResolve conflicts and maintain audit trailsModule assessment- Design advanced prompting strategies for production AI agentsDesign multiturn reasoning prompt architecturesImplement prompt injection defensesBuild system prompt frameworks for agent controlDesign multi-intervention guardrail architecturesImplement prompt versioning and optimizationAutomate prompt regression and optimizationDesign fine-tuning strategy and data pipelinesModule assessment- Build enterprise-grade tool ecosystems with MCP and Microsoft FoundryDesign production MCP server architectureBuild MCP servers with error handling and fallbackImplement tool selection and routing logicGovern tool dependencies and versioningModule assessment- Implement advanced RAG pipelines with Azure AI Search and Microsoft FoundryDesign hybrid search architecturesImplement reranking and context rankingDesign dynamic knowledge source routingOptimize chunking and embedding strategiesModule assessment- Design multi-agent memory architectures with Azure Cosmos DBExamine memory architecture patternsImplement semantic memory with vector storageOptimize memory retrieval and context injectionConfigure context window optimizationDesign memory retention and consolidationEnforce memory privacy and audit complianceModule assessment- Implement CI/CD pipelines for multi-agent systems with GitHub ActionsDesign multi-agent deployment pipelinesImplement progressive deployment strategiesConfigure multi-environment agent deployment strategiesAutomate rollback proceduresModule assessment- Secure multi-agent systems with Azure zero-trust architectureApply zero-trust identity to agent networksSecure agent access with JIT and workload identityDesign authentication flows and secrets lifecyclePrevent lateral movement in agent networksImplement tenant context propagation and data isolationValidate tenant boundaries and enforce encryptionConfigure compliance controls for regulated agent deploymentsModule assessment- Scale responsible AI governance with Azure AI Content Safety and Microsoft FoundryDesign fairness and bias monitoringImplement transparency and explainabilityConfigure privacy protection in multi-agent workflowsEstablish audit and accountability frameworksModule assessment- Govern the enterprise agent lifecycle in Microsoft FoundryDesign agent versioning and approval workflowsImplement usage quotas and rate limitingDesign cost allocation and chargeback modelsEstablish agent retirement and deprecation processesModule assessment- Implement distributed observability for multi-agent solutions with OpenTelemetryDesign distributed tracing for multi-agent solutionsImplement structured logging for agent decisionsConfigure telemetry aggregation and dashboardsBuild anomaly detection for agent behaviorModule assessment- Design evaluation frameworks for multi-agent solutions with Microsoft FoundryDefine multi-agent success metricsImplement LLM-as-judge evaluation for multi-agent systemsDesign synthetic test datasets for multi-agent evaluationBuild regression testing pipelines to detect agent driftModule assessment- Optimize multi-agent performance and cost in Microsoft FoundryDesign model routing for agent ecosystemsImplement multi-level caching strategiesOptimize token usage and context managementBalance quality, cost, and latency tradeoffsModule assessment- Design human-in-the-loop approval workflows with Power Automate and Microsoft TeamsDesign confidence-based escalation for human interventionImplement approval workflows for agent-initiated actionsBuild active learning from human feedbackConfigure audit workflows for regulated decisionsModule assessment- Debug and respond to production multi-agent incidents in AzureImplement agent replay for production debuggingDesign root cause analysis for agent failuresConfigure automated incident detection and remediationEstablish incident response and post-mortem processesModule assessment