Designing and Implementing Multi-Agent AI Solutions

categoryTopics Covered

Microsoft's own objectives, in the order the exam presents them. Each area links to its section on Microsoft Learn. Checked 2026-10-26.

1. 1Architect multi-agent solutions15–20%expand_more

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   Design logical architecture for multi-agent solutions

   • Decompose goals and objectives into workflows, agents, and tools
   • Design workflows, including agents, subagents, control loops, and human-in-the-loop
   • Specify agent personas, scopes, boundaries, autonomy levels, and behavioral guidelines
   • Specify tool scopes, permission boundaries, and authentication methods
   • Specify appropriate protocols for communication, including between multiple agents and between agents and other components
   • Design controls and functionality to support human-AI experience (HAX) and to adhere to responsible AI principles
   • Design short-term and long-term memory architectures, including context sharing
   • Match task demands to model family capacities

   Specify technology components to include in a multi-agent solution

   • Specify integration components to coordinate agent-to-agent communication, agent-to-tool interactions, and agent-to-knowledge-source routing across a multi-agent production system
   • Specify components for Zero Trust multi-agent solutions, including per-agent identity scoping, lateral movement prevention, and compliance control mapping for regulated deployments
   • Specify multi-tier state persistence components that address session state, shared team state, and long-term semantic memory with lifecycle and tenant-isolation policies
   • Specify compute components for multi-agent solutions that meet scalability, reliability, operational efficiency, security, and cost requirements
   • Specify observability components, including cross-service trace correlation, structured logging of agent reasoning paths, and agent replay capture for reproducible debugging
   • Specify monitoring components, including cross-agent coordination tracking, behavioral drift and quality regression detection, and automated remediation for agent failure patterns
   • Specify developer tools and environment to support the Software Development Lifecycle (SDLC), including dev containers, Visual Studio Code extensions, command-line interface (CLI), dependency management, and AI instructions
2. 2Develop multi-agent solutions in Azure30–35%expand_more

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   Design and implement advanced prompt engineering strategies in multi-agent solutions

   • Design and implement advanced prompt engineering techniques, including examples, dynamic context injection, defensive guidelines, and prompt lifecycle management
   • Design and implement context-aware multi-agent behaviors
   • Design and implement a strategy for fine-tuning agents and models, including data and frequency

   Design and implement agent memory, context management, and knowledge integration

   • Design and implement context management in single agents and between agents, including context accumulation, retrieval, injection, and compaction
   • Design and implement single-agent and multi-agent memory strategies, including security, compliance, lifecycle, storage, and session management
   • Design and implement a multi-agent RAG architecture, including chunking, embedding quality, and retrieval precision
   • Design and implement knowledge integration for multi-agent consumption, including search, RAG, MCP-available sources, and semantic search

   Build and integrate tool ecosystems

   • Integrate external resources, including function calling, specified tool use, and dynamic tool use
   • Design and build MCP servers and clients, including Azure Functions, Azure Logic Apps, and Azure API Management
   • Design tool error-handling and fallback mechanisms
   • Implement tool result validation and quality checks

   Implement multi-agent orchestration

   • Implement multi-agent orchestration patterns, including hub-and-spoke, sequential, parallel, peer-to-peer, and orchestrator-subagent
   • Implement human-in-the-loop processes, including approval workflows, overrides, and edge case handling
   • Design and implement a caching strategy, including prompt caching, semantic caching, and response caching
   • Implement scaling and control of multi-agent spawning, batching, and concurrent execution
   • Integrate existing agents into a multi-agent solution securely by using Agent2Agent (A2A) and/or MCP
   • Implement multi-agent orchestration frameworks, including Agent Framework, LangChain, and LangGraph
   • Implement advanced multi-agent capabilities by using Hugging Face Transformers
   • Design middleware for reusable capabilities across the solution, including logging, authorization, and exception handling
3. 3Evaluate, optimize, and monitor multi-agent solutions20–25%expand_more

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   Design and implement evaluation and validation strategies for multi-agent solutions

   • Design and implement a human review process to evaluate solutions in Foundry
   • Design and implement evaluations for memory, knowledge, tools, and prompts

   Optimize prompt and model performance for multi-agent solutions

   • Optimize task duration, including parallelism and rate limits
   • Diagnose context window issues, including sliding-window amnesia, summary drift, vector-only recall, and entity continuity issues
   • Design and implement continuous improvement, including large language model–as-a-judge (LLM-as-a-judge) frameworks, synthetic data generation, semantic optimization loops, and user feedback loops

   Design and implement observability and monitoring for multi-agent solutions

   • Design a strategy for monitoring reliability, including agent health, workflow failures, trace correlation, drift detection, quality regression, and remediation
   • Design a strategy for monitoring solution and platform availability, performance, reliability, and service-level agreement adherence
   • Design and implement a strategy for optimizing token usage, including token limits, loop controls, and tool calls
   • Design a strategy for monitoring and managing cost, including usage, quotas, allocations, and chargebacks
   • Implement tracing in Foundry, including tokens, prompts, correlation IDs, alerting, and execution tracking
4. 4Secure, govern, and deploy multi-agent solutions20–25%expand_more

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   Design and implement security for multi-agent solutions

   • Specify and implement resource access, including identity-based access, network boundaries, access control policies, and role-based access control (RBAC)
   • Design and implement authentication flows in a multi-agent solution, including user impersonation, on-behalf-of, API keys, and OAuth 2.0
   • Design the secrets management and retrieval approach for multi-agent solutions by using Azure Key Vault, including secrets, certificates, key rotation, role-based access to secrets, and encryption
   • Implement shift-left security principles, including the AI Red Teaming Agent in Foundry

   Design and implement guardrails for multi-agent solutions

   • Design and implement a multi-intervention guardrail strategy covering user inputs, tool calls, tool responses, and outputs
   • Design and implement custom guardrails for domain-specific constraints
   • Design guardrail testing and validation, including using synthetic data

   Deploy multi-agent solutions to Azure

   • Choose a release methodology, including Development-Test-Acceptance-Production (DTAP), blue/green, and canary
   • Design and implement multi-environment release strategies, including rollback strategies, release management, and rollout
   • Design and implement a strategy for testing, including unit tests, regression tests, integration tests, and automated evaluations
   • Specify continuous integration and continuous deployment (CI/CD) requirements for integration and release, including infrastructure-as-code deployments and testing