Capability Development / AI
Design and Implement Multi-Agent AI (AI-500)
This program helps learners understand, design and implement enterprise-ready multi-agent AI solutions, moving from core agentic AI concepts to advanced implementation areas such as AI agent development, tool integration, RAG, memory, orchestration, security, monitoring, deployment and governance across Microsoft Foundry, Azure AI services, MCP, RAG and LangGraph workflows.
Program Overview
This program helps learners understand, design and implement enterprise-ready multi-agent AI solutions, moving from core agentic AI concepts to advanced implementation areas such as AI agent development, tool integration, RAG, memory, orchestration, security, monitoring, deployment and governance across Microsoft Foundry, Azure AI services, MCP, RAG and LangGraph workflows.
Audience Profile
For AI engineers and solution architects who need to design and build secure, scalable, production-ready multi-agent AI solutions for real enterprise scenarios, integrating tools, enterprise data and RAG pipelines while applying security, evaluation and operational best practices.
Course Syllabus
Module 1: Multi-Agent AI Fundamentals and the Foundry Ecosystem
Topics
- Agentic AI, autonomous agents and multi-agent collaboration
- Single-agent, multi-agent and workflow-based patterns
- Azure AI services and Microsoft Foundry projects, models, agents and tools
Module 2: Designing Multi-Agent Architectures
Topics
- Agent personas, responsibilities and decision boundaries
- Specialist, coordinator, critic, planner, executor and reviewer patterns
- Communication patterns: hub-and-spoke, supervisor-worker, peer-to-peer, event-driven
Module 3: Building AI Agents with Microsoft Foundry Agent Service
Topics
- Agent instructions, models, tools and runtime settings
- Prompt controls, guardrails and safety boundaries
- Prompt versioning, peer review and regression testing
Module 4: Tool Integration and Model Context Protocol (MCP)
Topics
- Connecting agents to APIs, databases and business applications
- Tool discovery, parameter validation and fallback handling
- MCP server architecture, identity, authorization and governance
Module 5: Retrieval-Augmented Generation (RAG)
Topics
- Grounding, chunking, embeddings and vector search
- Hybrid search, semantic ranking and reranking with Azure AI Search
- Evaluating groundedness, relevance and source quality
Module 6: Memory, Orchestration and LangGraph Workflows
Topics
- Short-term, long-term, semantic and episodic memory
- Planning, reflection, critique and retry loops
- Graph-based orchestration and production LangGraph workflows
Module 7: Security, Responsible AI and Evaluation
Topics
- Zero Trust principles, managed identities and least privilege
- Prompt injection, data leakage and unauthorized tool-use defenses
- Observability, tracing, agent evaluation and quality assurance
Module 8: Deployment, Governance and Operations
Topics
- CI/CD pipelines, approvals, testing and rollback
- Enterprise integration with Microsoft 365 and Teams
- AI governance, lifecycle management, cost and incident response