Course
digicode: AI500
Design and implement multi-agent AI solutions – Intensive Training
Course facts
Download as PDF- Designing scalable and maintainable multi-agent systems in Azure by applying core agentic architectures, orchestration patterns, and communication strategies
- Developing production-grade multi-agent capabilities leveraging Microsoft Foundry's framework and best practices
- Establishing enterprise-grade deployment pipelines and governance frameworks using CI/CD, zero-trust security, responsible AI compliance, and lifecycle management
- Monitoring system operations through distributed OpenTelemetry observability with Azure Monitor Application Insights and systematic LLM-as-judge evaluations in Azure AI Foundry
- Optimizing multi-agent performance, cost efficiency, human-in-the-loop workflows, and incident debugging procedures for reliable enterprise operation
1 Architect production-grade multi-agent AI solutions in Azure
Production-grade multi-agent AI solutions require a deep understanding of agentic architectures, orchestration patterns, and communication strategies. This module will guide you through the essential concepts and best practices for building scalable and maintainable multi-agent systems in Azure.
2 Build production-grade multi-agent capabilities with Microsoft Foundry
Production-grade multi-agent capabilities require a deep understanding of agentic architectures, orchestration patterns, and communication strategies. This module will guide you through the essential concepts and best practices for building scalable and maintainable multi-agent systems with Microsoft Foundry.
3 Deploy and govern enterprise agentic AI solutions on Azure
Deploying and governing enterprise agentic AI solutions on Azure requires a structured approach to CI/CD pipelines, zero-trust security, responsible AI compliance, and agent lifecycle management. This module guides you through the operational and governance capabilities required to run multi-agent systems reliably and safely at enterprise scale.
4 Monitor, evaluate, and operate multi-agent AI solutions in Azure
Operating production-grade multi-agent AI solutions in Azure requires end-to-end visibility, systematic quality measurement, and structured incident response. This module guides you through distributed OpenTelemetry observability with Azure Monitor Application Insights, LLM-as-judge agent evaluation using Azure AI Foundry, multi-agent cost and performance optimization, enterprise human-in-the-loop approval workflows with Power Automate, and structured debugging procedures for production AI incidents.
Candidates for this course, are expert-level practitioners who have subject matter expertise in designing, building, and optimizing scalable, production‑ready, multi-agent AI systems, solutions and workflows. Out of your job role, you work closely with developers, machine learning engineers, platform engineers, data scientists, and business stakeholders to translate complex requirements into production-ready, multi-agent solutions.
- Working experience with Microsoft Foundry Agent Service and Microsoft Agent Framework
- Familiarity with foundational multi-agent orchestration patterns (sequential, concurrent, group chat, handoff)
- Python programming proficiency, including async patterns and REST API consumption
- Experience consuming MCP tools and custom function tools with Microsoft Foundry Agent Service
- Experience with system prompt design for agent persona and behavior control
- Working experience with GitHub Actions for CI/CD pipelines
- Experience with OpenTelemetry instrumentation and Azure Monitor / Log Analytics workspaces
The following courses or equivalent knowledge is required:
This intensive training prepares you for:
- Exam: «AI-500: Designing and Implementing Multi-Agent AI Solutions» for the
- Certification: «Microsoft Certified: Multi-Agent AI Solutions Expert»