Build production-grade multi-agent capabilities with Microsoft Foundry
At a glance
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Production-grade multi-agent capabilities require a deep understanding of agentic architectures, orchestration patterns, and communication strategies. This learning path will guide you through the essential concepts and best practices for building scalable and maintainable multi-agent systems with Microsoft Foundry.
Prerequisites
- Completion of AI-103 "Develop AI agents on Azure" or equivalent experience
- Experience consuming MCP tools and custom function tools with Microsoft Foundry Agent Service
- Familiarity with classic RAG retrieval patterns using Azure AI Search (hybrid search, semantic ranking); experience with Foundry IQ agentic retrieval is beneficial but not required
- Experience with system prompt design for agent persona and behavior control
- Python proficiency with the Azure AI SDK and Azure OpenAI SDK
Achievement Code
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Modules in this learning path
Design advanced prompting strategies for production AI agents in Microsoft Foundry. Implement multiturn reasoning architectures with dynamic context injection, build layered prompt injection defenses for untrusted input environments, design system prompt frameworks for complex agent persona and constraint control, and implement prompt versioning and optimization workflows.
Build enterprise-grade tool ecosystems for production multi-agent systems with MCP and Microsoft Foundry. Develop custom MCP servers with authentication and production error handling, implement dynamic tool selection and routing logic, design tool result validation and fallback pipelines, and govern tool ecosystems with dependency management and versioning strategies.
Implement advanced RAG pipelines for production AI agents using Azure AI Search and Microsoft Foundry. Design hybrid search architectures combining BM25 keyword and semantic vector retrieval, implement reranking strategies for high-precision knowledge access, configure dynamic knowledge source routing across multiple indexes, and optimize chunking and embedding strategies.
Design multi-agent memory architectures for production AI systems using Azure Cosmos DB. Implement semantic memory with vector storage for knowledge retention across sessions, configure context window optimization strategies that balance memory richness with cost and latency, and design memory expiration and pruning policies for production deployments.