{"id":254857,"date":"2026-09-17T17:19:53","date_gmt":"2026-09-17T15:19:53","guid":{"rendered":"https:\/\/www.itta.net\/?post_type=formations&#038;p=254857"},"modified":"2026-09-17T17:50:09","modified_gmt":"2026-09-17T15:50:09","slug":"design-implement-multi-agent-ai-solutions-ai-500","status":"publish","type":"formations","link":"https:\/\/www.itta.net\/en\/trainings\/artificial-intelligence\/ai-development-and-llm\/design-implement-multi-agent-ai-solutions-ai-500\/","title":{"rendered":"Design and implement multi-agent AI solutions (AI-500)"},"content":{"rendered":"<p>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.<\/p>\n<p>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.<\/p>\n","protected":false},"featured_media":0,"parent":0,"menu_order":0,"template":"","editeurs":[5032,1488],"domaine":[4248,4251,2982,4273],"class_list":["post-254857","formations","type-formations","status-publish","hentry","editeurs-azure-ai-services","editeurs-microsoft","domaine-ai-agent-creation-and-automation","domaine-ai-development-and-llm","domaine-artificial-intelligence","domaine-azure-ai-platforms"],"acf":{"reference":"AI-500T00","duree_nombre":4,"duree_unite":"jour","prix_virtuel":3000,"prix_presentiel":3200,"prix_blended":"","prix_elearning":"","views":7,"niveau":["Avanc\u00e9"],"role":[],"certifiant":["Certifiant"],"pdus":"","lieux":["Gen\u00e8ve","Lausanne"],"formats_dapprentissage":["presentiel","virtuel"],"examen_inclus":"Optionnel","garanti":[],"financement":["Temptraining"],"description_temptraining":"","description_caf":"","description_title":"Design and Implement Multi-Agent AI Solutions Training (AI-500)","objectifs":"<ul>\n<li>Structure the logical architecture of a multi-agent AI solution on Azure<\/li>\n<li>Implement stateful agentic loops and the main orchestration patterns in Microsoft Foundry<\/li>\n<li>Connect your agents to your tools and data with the MCP protocol and RAG pipelines<\/li>\n<li>Organise shared memory across agents with Azure Cosmos DB<\/li>\n<li>Apply security and governance good practices to agent networks<\/li>\n<li>Prepare your agents for production: deployment, observability and evaluation<\/li>\n<\/ul>\n","connaissances":"<ul>\n<li>Hands-on practice building AI applications on Azure, preferably in Python<\/li>\n<li>Some familiarity with Microsoft Foundry and Azure AI services<\/li>\n<li>The Develop AI apps and agents on Azure (AI-103) training is good preparation, though not mandatory<\/li>\n<\/ul>\n","profils_participants":"<ul>\n<li>AI engineers<\/li>\n<li>AI solution architects<\/li>\n<li>Intelligent application developers<\/li>\n<li>Machine learning engineers<\/li>\n<li>Platform and DevOps engineers running agentic systems<\/li>\n<li>Edge AI engineers<\/li>\n<\/ul>\n","examen":"<ul>\n\n \t<li>This course prepares you for the AI-500 certification: Microsoft Certified: Multi-Agent AI Solutions Expert.<\/li>\n\n<\/ul>\n","documentation":"<ul>\n <li>Access to Microsoft Learn, Microsoft's online learning platform, offering interactive resources and educational content to deepen your knowledge and develop your technical skills.<\/li>\n<\/ul>\n","lab__exercices":"<ul>\n <li>This course provides you with exclusive access to the official Microsoft lab, allowing you to put your skills into practice in a professional environment.<\/li>\n<\/ul>\n","contenu_cours":"<p><strong>Module 1 : Design stateful agentic loops with Microsoft Foundry Agent Service<\/strong><\/p>\n<ul>\n<li>Examine production agentic loop architecture<\/li>\n<li>Examine the Foundry Responses API and agents v2 model<\/li>\n<li>Implement agent reflection and planning cycles<\/li>\n<li>Design session state and context management<\/li>\n<li>Implement fork-based sessions and conversation resumption<\/li>\n<li>Migrate stateful agentic loops from agents v1 to agents v2<\/li>\n<\/ul>\n<p><strong>Module 2 : Implement advanced multi-agent orchestration patterns in Microsoft Foundry<\/strong><\/p>\n<ul>\n<li>Differentiate agentic AI from multi-agent AI architectures<\/li>\n<li>Examine advanced orchestration architectures<\/li>\n<li>Implement hub-and-spoke orchestration<\/li>\n<li>Design parallel agent spawning and synchronization<\/li>\n<li>Compare orchestration frameworks<\/li>\n<\/ul>\n<p><strong>Module 3 : Apply task decomposition and agent collaboration strategies<\/strong><\/p>\n<ul>\n<li>Design prompt chaining workflows<\/li>\n<li>Implement dynamic adaptive task decomposition<\/li>\n<li>Design agent handoff message schemas<\/li>\n<li>Ensure handoff reliability and context preservation<\/li>\n<li>Optimize decomposition granularity<\/li>\n<\/ul>\n<p><strong>Module 4 : Design enterprise-scale agent communication with the A2A protocol<\/strong><\/p>\n<ul>\n<li>Design A2A agent ecosystems at scale<\/li>\n<li>Implement distributed shared state management<\/li>\n<li>Design context isolation and sharing strategies<\/li>\n<li>Build conflict detection and resolution mechanisms<\/li>\n<li>Resolve conflicts and maintain audit trails<\/li>\n<\/ul>\n<p><strong>Module 5 : Design advanced prompting strategies for production AI agents<\/strong><\/p>\n<ul>\n<li>Design multiturn reasoning prompt architectures<\/li>\n<li>Implement prompt injection defenses<\/li>\n<li>Build system prompt frameworks for agent control<\/li>\n<li>Design multi-intervention guardrail architectures<\/li>\n<li>Implement prompt versioning and optimization<\/li>\n<li>Automate prompt regression and optimization<\/li>\n<li>Design fine-tuning strategy and data pipelines<\/li>\n<\/ul>\n<p><strong>Module 6 : Build enterprise-grade tool ecosystems with MCP and Microsoft Foundry<\/strong><\/p>\n<ul>\n<li>Design production MCP server architecture<\/li>\n<li>Build MCP servers with error handling and fallback<\/li>\n<li>Implement tool selection and routing logic<\/li>\n<li>Govern tool dependencies and versioning<\/li>\n<\/ul>\n<p><strong>Module 7 : Implement advanced RAG pipelines with Azure AI Search and Microsoft Foundry<\/strong><\/p>\n<ul>\n<li>Design hybrid search architectures<\/li>\n<li>Implement reranking and context ranking<\/li>\n<li>Design dynamic knowledge source routing<\/li>\n<li>Optimize chunking and embedding strategies<\/li>\n<\/ul>\n<p><strong>Module 8 : Design multi-agent memory architectures with Azure Cosmos DB<\/strong><\/p>\n<ul>\n<li>Examine memory architecture patterns<\/li>\n<li>Implement semantic memory with vector storage<\/li>\n<li>Optimize memory retrieval and context injection<\/li>\n<li>Configure context window optimization<\/li>\n<li>Design memory retention and consolidation<\/li>\n<li>Enforce memory privacy and audit compliance<\/li>\n<\/ul>\n<p><strong>Module 9 : Implement CI\/CD pipelines for multi-agent systems with GitHub Actions<\/strong><\/p>\n<ul>\n<li>Design multi-agent deployment pipelines<\/li>\n<li>Implement progressive deployment strategies<\/li>\n<li>Configure multi-environment agent deployment strategies<\/li>\n<li>Automate rollback procedures<\/li>\n<\/ul>\n<p><strong>Module 10 : Secure multi-agent systems with Azure zero-trust architecture<\/strong><\/p>\n<ul>\n<li>Apply zero-trust identity to agent networks<\/li>\n<li>Secure agent access with JIT and workload identity<\/li>\n<li>Design authentication flows and secrets lifecycle<\/li>\n<li>Prevent lateral movement in agent networks<\/li>\n<li>Implement tenant context propagation and data isolation<\/li>\n<li>Validate tenant boundaries and enforce encryption<\/li>\n<li>Configure compliance controls for regulated agent deployments<\/li>\n<\/ul>\n<p><strong>Module 11 : Scale responsible AI governance with Azure AI Content Safety<\/strong><\/p>\n<ul>\n<li>Design fairness and bias monitoring<\/li>\n<li>Implement transparency and explainability<\/li>\n<li>Configure privacy protection in multi-agent workflows<\/li>\n<li>Establish audit and accountability frameworks<\/li>\n<\/ul>\n<p><strong>Module 12 : Govern the enterprise agent lifecycle in Microsoft Foundry<\/strong><\/p>\n<ul>\n<li>Design agent versioning and approval workflows<\/li>\n<li>Implement usage quotas and rate limiting<\/li>\n<li>Design cost allocation and chargeback models<\/li>\n<li>Establish agent retirement and deprecation processes<\/li>\n<\/ul>\n<p><strong>Module 13 : Implement distributed observability for multi-agent solutions with OpenTelemetry<\/strong><\/p>\n<ul>\n<li>Design distributed tracing for multi-agent solutions<\/li>\n<li>Implement structured logging for agent decisions<\/li>\n<li>Configure telemetry aggregation and dashboards<\/li>\n<li>Build anomaly detection for agent behavior<\/li>\n<\/ul>\n<p><strong>Module 14 : Design evaluation frameworks for multi-agent solutions<\/strong><\/p>\n<ul>\n<li>Define multi-agent success metrics<\/li>\n<li>Implement LLM-as-judge evaluation for multi-agent systems<\/li>\n<li>Design synthetic test datasets for multi-agent evaluation<\/li>\n<li>Build regression testing pipelines to detect agent drift<\/li>\n<\/ul>\n<p><strong>Module 15 : Optimize multi-agent performance and cost in Microsoft Foundry<\/strong><\/p>\n<ul>\n<li>Design model routing for agent ecosystems<\/li>\n<li>Implement multi-level caching strategies<\/li>\n<li>Optimize token usage and context management<\/li>\n<li>Balance quality, cost, and latency tradeoffs<\/li>\n<\/ul>\n<p><strong>Module 16 : Design human-in-the-loop approval workflows with Power Automate and Microsoft Teams<\/strong><\/p>\n<ul>\n<li>Design confidence-based escalation for human intervention<\/li>\n<li>Implement approval workflows for agent-initiated actions<\/li>\n<li>Build active learning from human feedback<\/li>\n<li>Configure audit workflows for regulated decisions<\/li>\n<\/ul>\n<p><strong>Module 17 : Debug and respond to production multi-agent incidents in Azure<\/strong><\/p>\n<ul>\n<li>Implement agent replay for production debugging<\/li>\n<li>Design root cause analysis for agent failures<\/li>\n<li>Configure automated incident detection and remediation<\/li>\n<li>Establish incident response and post-mortem processes<\/li>\n<\/ul>\n","cours_recommandes":[253301,253302,254460],"infos_additionnelles":"<h3>Multi-agent systems, the next frontier of enterprise AI<\/h3>\n<p>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.<\/p>\n<p>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.<\/p>\n<h3>What the Multi-Agent AI Solutions Expert certification validates<\/h3>\n<p>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.<\/p>\n<p>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.<\/p>\n<h3>Where AI-500 sits in the Azure AI learning path<\/h3>\n<p>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.<\/p>\n<ul>\n<li>AI-102 and AI-103: develop AI applications and agents on Azure, the technical foundation expected before AI-500<\/li>\n<li>AI-200: design complete cloud AI solutions on Azure<\/li>\n<li>AI-300: operationalize machine learning and generative AI solutions, the MLOps and GenAIOps angle<\/li>\n<li>AI-500: design and operate production multi-agent systems, expert level<\/li>\n<\/ul>\n<p>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.<\/p>\n<h3>Microsoft Foundry as the technical backbone<\/h3>\n<p>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.<\/p>\n<p>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.<\/p>\n<h3>Preparing effectively<\/h3>\n<p>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.<\/p>\n<p>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'.<\/p>\n<h3>Frequently asked questions<\/h3>\n<p><strong>Do I need AI-102 or AI-103 before taking AI-500?<\/strong><\/p>\n<p>It is not a requirement. Hands-on practice building AI applications on Azure is enough to follow comfortably, and the trainer adapts the pace to the group.<\/p>\n<p><strong>Is the exam included in the training?<\/strong><\/p>\n<p>The exam is optional and booked separately with Microsoft. We help you choose the right time slot and prepare for it.<\/p>\n<p><strong>How does this differ from the Designing a Multi-Agent AI Architecture training?<\/strong><\/p>\n<p>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.<\/p>\n<p><strong>Does the course cover only Microsoft Foundry, or open source frameworks too?<\/strong><\/p>\n<p>The official programme is built on Microsoft Foundry and Azure services. One module does compare the main orchestration frameworks on the market, which helps you position your own technical choices if you already work with other tooling.<\/p>\n<p><strong>Should I bring a business case with me?<\/strong><\/p>\n<p>It is not required, but it is strongly recommended. The labs become far more valuable when you test the course patterns against an agentic architecture you already know.<\/p>\n","inclus_dans_ce_cours_virtuel":"<ul>\n <li>Training provided by a certified trainer<\/li>\n <li>180 days of access to Official Microsoft labs<\/li>\n <li>Official documentation in digital format<\/li>\n <li>Official Microsoft achievement badge<\/li>\n<\/ul>\n","inclus_dans_ce_cours_presentiel":"<ul>\n <li>Training provided by a certified trainer<\/li>\n <li>180 days of access to Official Microsoft labs<\/li>\n <li>Official documentation in digital format<\/li>\n <li>Official Microsoft achievement badge<\/li>\n<\/ul>\n","inclus_dans_ce_cours_blended":"","inclus_dans_ce_cours_elearning":"","inclus_dans_ce_cours_surmesure":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>AI-500 Multi-Agent AI Solutions Training - ITTA<\/title>\n<meta name=\"description\" content=\"AI-500 training: design and operate multi-agent AI solutions on Azure in 4 days with official Microsoft labs in Geneva. 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