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

Ref. AI-500T00
Duration:
4
 jours
Exam:
Optionnel
Level:
Avancé

Design and Implement Multi-Agent AI Solutions Training (AI-500)

Orchestrating several AI agents that cooperate in production is an architecture exercise, not a prompting one. The AI-500 training teaches you to design, build and operate multi-agent AI solutions with Microsoft Foundry and Azure: logical architecture, tool ecosystems, orchestration, shared memory, security and governance.

Over four days, you work on stateful agentic loops, hub-and-spoke orchestration patterns, the A2A protocol, MCP servers, advanced RAG pipelines with Azure AI Search and memory architectures built on Azure Cosmos DB. You then move to production concerns: CI/CD pipelines with GitHub Actions, zero-trust architecture, distributed observability with OpenTelemetry and evaluation frameworks. Every topic is worked through on a complete enterprise scenario, from the first agent to the production incident.

This expert-level course prepares you for the AI-500 exam and the Microsoft Certified: Multi-Agent AI Solutions Expert certification. It is delivered in Geneva and Lausanne, or in a virtual classroom, by a Microsoft Certified Trainer.

Participant Profiles

  • AI engineers
  • AI solution architects
  • Intelligent application developers
  • Machine learning engineers
  • Platform and DevOps engineers running agentic systems
  • Edge AI engineers

Objectives

  • Design the logical architecture of production-ready multi-agent AI solutions on Azure
  • Implement stateful agentic loops and advanced orchestration patterns in Microsoft Foundry
  • Build enterprise-grade tool ecosystems with the MCP protocol and advanced RAG pipelines
  • Design multi-agent memory architectures with Azure Cosmos DB and optimize the context window
  • Secure agent networks with zero-trust architecture and govern the agent lifecycle
  • Deploy multi-agent systems with GitHub Actions CI/CD pipelines and progressive deployment strategies
  • Implement distributed observability, continuous evaluation and cost optimization
  • Debug production incidents and design human-in-the-loop approval workflows

Prerequisites

  • Solid experience developing AI solutions on Azure, equivalent to the AI-102 or AI-103 certification
  • Strong Python skills and practice with AI agent SDKs
  • Working knowledge of Microsoft Foundry and Azure AI services
  • Understanding of distributed cloud architectures, Azure identity and DevOps principles
  • The Develop AI apps and agents on Azure (AI-103) training is the recommended preparation

Course Content

Module 1 : Design stateful agentic loops with Microsoft Foundry Agent Service

  • Examine production agentic loop architecture
  • Examine the Foundry Responses API and agents v2 model
  • Implement agent reflection and planning cycles
  • Design session state and context management
  • Implement fork-based sessions and conversation resumption
  • Migrate stateful agentic loops from agents v1 to agents v2

Module 2 : Implement advanced multi-agent orchestration patterns in Microsoft Foundry

  • Differentiate agentic AI from multi-agent AI architectures
  • Examine advanced orchestration architectures
  • Implement hub-and-spoke orchestration
  • Design parallel agent spawning and synchronization
  • Compare orchestration frameworks

Module 3 : Apply task decomposition and agent collaboration strategies

  • Design prompt chaining workflows
  • Implement dynamic adaptive task decomposition
  • Design agent handoff message schemas
  • Ensure handoff reliability and context preservation
  • Optimize decomposition granularity

Module 4 : Design enterprise-scale agent communication with the A2A protocol

  • Design A2A agent ecosystems at scale
  • Implement distributed shared state management
  • Design context isolation and sharing strategies
  • Build conflict detection and resolution mechanisms
  • Resolve conflicts and maintain audit trails

Module 5 : Design advanced prompting strategies for production AI agents

  • Design multiturn reasoning prompt architectures
  • Implement prompt injection defenses
  • Build system prompt frameworks for agent control
  • Design multi-intervention guardrail architectures
  • Implement prompt versioning and optimization
  • Automate prompt regression and optimization
  • Design fine-tuning strategy and data pipelines

Module 6 : Build enterprise-grade tool ecosystems with MCP and Microsoft Foundry

  • Design production MCP server architecture
  • Build MCP servers with error handling and fallback
  • Implement tool selection and routing logic
  • Govern tool dependencies and versioning

Module 7 : Implement advanced RAG pipelines with Azure AI Search and Microsoft Foundry

  • Design hybrid search architectures
  • Implement reranking and context ranking
  • Design dynamic knowledge source routing
  • Optimize chunking and embedding strategies

Module 8 : Design multi-agent memory architectures with Azure Cosmos DB

  • Examine memory architecture patterns
  • Implement semantic memory with vector storage
  • Optimize memory retrieval and context injection
  • Configure context window optimization
  • Design memory retention and consolidation
  • Enforce memory privacy and audit compliance

Module 9 : Implement CI/CD pipelines for multi-agent systems with GitHub Actions

  • Design multi-agent deployment pipelines
  • Implement progressive deployment strategies
  • Configure multi-environment agent deployment strategies
  • Automate rollback procedures

Module 10 : Secure multi-agent systems with Azure zero-trust architecture

  • Apply zero-trust identity to agent networks
  • Secure agent access with JIT and workload identity
  • Design authentication flows and secrets lifecycle
  • Prevent lateral movement in agent networks
  • Implement tenant context propagation and data isolation
  • Validate tenant boundaries and enforce encryption
  • Configure compliance controls for regulated agent deployments

Module 11 : Scale responsible AI governance with Azure AI Content Safety

  • Design fairness and bias monitoring
  • Implement transparency and explainability
  • Configure privacy protection in multi-agent workflows
  • Establish audit and accountability frameworks

Module 12 : Govern the enterprise agent lifecycle in Microsoft Foundry

  • Design agent versioning and approval workflows
  • Implement usage quotas and rate limiting
  • Design cost allocation and chargeback models
  • Establish agent retirement and deprecation processes

Module 13 : Implement distributed observability for multi-agent solutions with OpenTelemetry

  • Design distributed tracing for multi-agent solutions
  • Implement structured logging for agent decisions
  • Configure telemetry aggregation and dashboards
  • Build anomaly detection for agent behavior

Module 14 : Design evaluation frameworks for multi-agent solutions

  • Define multi-agent success metrics
  • Implement LLM-as-judge evaluation for multi-agent systems
  • Design synthetic test datasets for multi-agent evaluation
  • Build regression testing pipelines to detect agent drift

Module 15 : Optimize multi-agent performance and cost in Microsoft Foundry

  • Design model routing for agent ecosystems
  • Implement multi-level caching strategies
  • Optimize token usage and context management
  • Balance quality, cost, and latency tradeoffs

Module 16 : Design human-in-the-loop approval workflows with Power Automate and Microsoft Teams

  • Design confidence-based escalation for human intervention
  • Implement approval workflows for agent-initiated actions
  • Build active learning from human feedback
  • Configure audit workflows for regulated decisions

Module 17 : Debug and respond to production multi-agent incidents in Azure

  • Implement agent replay for production debugging
  • Design root cause analysis for agent failures
  • Configure automated incident detection and remediation
  • Establish incident response and post-mortem processes

Documentation

  • Access to Microsoft Learn, Microsoft’s online learning platform, offering interactive resources and educational content to deepen your knowledge and develop your technical skills.

Lab / Exercises

  • This course provides you with exclusive access to the official Microsoft lab, allowing you to put your skills into practice in a professional environment.

Exam

  • This course prepares you for the AI-500 certification: Microsoft Certified: Multi-Agent AI Solutions Expert.

Complementary Courses

Eligible Funding

ITTA is a partner of a continuing education fund dedicated to temporary workers. This fund can subsidize your training, provided that you are subject to the “Service Provision” collective labor agreement (CCT) and meet certain conditions, including having worked at least 88 hours in the past 12 months.

Additional Information

Multi-agent systems, the next frontier of enterprise AI

A single agent can answer, retrieve information and call a tool. A multi-agent system splits a complex goal across several specialised agents that coordinate, hand over context and keep each other in check. Moving from one to the other changes the nature of the problem: it is no longer about writing a good prompt, but about designing a distributed architecture with its exchange protocols, shared state, security boundaries and guardrails. That is exactly the scope of this course, aimed at practitioners who already build agents and now need to run them at scale.

The real difficulty never shows up in a demo. It appears when two agents write to the same state, when a handoff loses the context that mattered, when a prompt injection spreads from one agent to the next, or when the token bill grows without anyone knowing which agent caused it. The four days address these situations one by one, with the Azure services designed to handle them.

What the Multi-Agent AI Solutions Expert certification validates

Microsoft introduced an expert-level certification dedicated to multi-agent solutions. The AI-500 exam covers four areas: designing the logical architecture, building and integrating tool ecosystems, implementing multi-agent orchestration, and integrating monitoring, security and governance. The target profile is an AI engineer or architect who works alongside developers, machine learning engineers, platform teams and business stakeholders to turn complex requirements into systems that actually run.

The certification and its exam are currently offered in beta by Microsoft. Skill objectives may still evolve before the final release, and beta exam results are published once the calibration period ends. Our trainers follow these updates and adapt the content as official revisions land.

Where AI-500 sits in the Azure AI learning path

Microsoft’s AI track has settled into a few clear steps, and AI-500 sits at the top. Picking the right step avoids attending a course that is either too advanced or too broad.

  • AI-102 and AI-103: develop AI applications and agents on Azure, the technical foundation expected before AI-500
  • AI-200: design complete cloud AI solutions on Azure
  • AI-300: operationalize machine learning and generative AI solutions, the MLOps and GenAIOps angle
  • AI-500: design and operate production multi-agent systems, expert level

If you are still building your first agents, start with the Develop AI apps and agents on Azure (AI-103) training. If your challenge is industrialising models rather than coordinating agents, the Operationalize Machine Learning and Generative AI Solutions (AI-300) training is a better fit.

Microsoft Foundry as the technical backbone

Microsoft Foundry runs through all four days. You work with the Agent service and its agents v2 model, the Responses API, session management and conversation resumption, then the orchestration patterns that hold a complete system together. Around that backbone come the Azure services that make a solution operable: Azure AI Search for hybrid RAG pipelines, Azure Cosmos DB for semantic and vector memory, Azure AI Content Safety for governance, OpenTelemetry for distributed tracing, GitHub Actions for deployment and Power Automate for human approvals.

The MCP and A2A protocols take up a significant part of the programme. The first shapes how your agents reach tools and enterprise data, the second how they talk to each other at scale. Those two building blocks are what separate a proof of concept from an enterprise architecture.

Preparing effectively

The best preparation is to take an existing agentic solution and put the course questions to it: what happens if an agent fails mid-handoff, how is one customer’s context isolated from another’s, what trace remains of a decision made three weeks ago, who approves a sensitive action and on what basis. Arriving with that real case in mind makes the labs far more productive.

After the training, 180 days of access to the official Microsoft labs let you replay the exercises on your own scenario before sitting the exam. Our sessions keep group sizes small, so there is time to compare your architecture with the trainer’s and with the other participants’.

Frequently asked questions

Do I need AI-102 or AI-103 before taking AI-500?

It is not a formal requirement, but the expected level matches those certifications. Without hands-on experience building agents in Python on Azure, the step up is steep.

Is the exam included in the training?

The exam is optional and booked separately with Microsoft. We help you choose the right time slot and prepare for it.

How does this differ from the Designing a Multi-Agent AI Architecture training?

Our multi-agent architecture course covers principles, patterns and tooling across platforms. AI-500 is an official Microsoft course, entirely focused on Microsoft Foundry and Azure services, and it leads to a certification.

Is the course available on site in Geneva and Lausanne?

Yes, in both of our centres, and as a virtual classroom with the same trainer and the same labs.

Can it be funded through Temptraining?

Yes, if you are covered by a collective labour agreement. Our team helps you put the application together.

In which language is the course delivered?

Sessions are delivered in English or French depending on the group. Official Microsoft courseware and labs are in English, as for every Microsoft certification track.

Prix de l'inscription
CHF 3'000.-
Inclus dans ce cours
  • Training provided by a certified trainer
  • 180 days of access to Official Microsoft labs
  • Official documentation in digital format
  • Official Microsoft achievement badge
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Route des jeunes 35
1227 Carouge, Suisse

Opening hours

Monday to Friday, from 8:30 am to 06:00 pm.

Contact us

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