{"id":254464,"date":"2026-09-03T09:45:28","date_gmt":"2026-09-03T07:45:28","guid":{"rendered":"https:\/\/www.itta.net\/?post_type=formations&#038;p=254464"},"modified":"2026-09-03T09:45:30","modified_gmt":"2026-09-03T07:45:30","slug":"developing-in-agentic-ai-systems-gh-600","status":"publish","type":"formations","link":"https:\/\/www.itta.net\/en\/trainings\/artificial-intelligence\/ai-development-and-llm\/developing-in-agentic-ai-systems-gh-600\/","title":{"rendered":"Developing in Agentic AI Systems (GH-600)"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">The GitHub Agentic AI training (GH-600) teaches you to design agent systems that work inside your development pipeline without losing control over what lands in your repositories. In one day you move from the raw observation (an agent opens pull requests, runs tests, fixes code) to a deliberate architecture: scoped responsibilities, defined inputs and outputs, explicit success criteria. The official GitHub plan covers six modules, from defining agentic AI to the guardrails required in production.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Agents governed by native GitHub mechanisms<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">You work on what separates an experiment from a reliable system: the Model Context Protocol to connect tools, GitHub Actions to orchestrate several agents, isolation by branch and by permission, memory and context drift management, then the evaluation signals that show whether an agent is improving or degrading. The final module covers governance: risk-based autonomy boundaries, human review at sensitive steps, least privilege and full traceability of every action.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Delivered by a certified instructor at ITTA in Geneva or Lausanne, this day targets developers and DevOps engineers already comfortable with Git, pull requests and GitHub Copilot. It covers the scope of the GH-600 exam and prepares you for the GitHub Certified: Agentic AI Developer certification.<\/p>\n","protected":false},"featured_media":0,"parent":0,"menu_order":0,"template":"","editeurs":[1488,1490],"domaine":[4251,2982,4271],"class_list":["post-254464","formations","type-formations","status-publish","hentry","editeurs-microsoft","editeurs-microsoft-azure-microsoft","domaine-ai-development-and-llm","domaine-artificial-intelligence","domaine-development-with-llms-and-agents"],"acf":{"reference":"GH-600T00","duree_nombre":1,"duree_unite":"jour","prix_virtuel":850,"prix_presentiel":900,"prix_blended":"","prix_elearning":"","views":14,"niveau":["Interm\u00e9diaire"],"role":[],"certifiant":["Certifiant"],"pdus":"","lieux":["Gen\u00e8ve","Lausanne"],"formats_dapprentissage":["presentiel","virtuel"],"examen_inclus":"Optionnel","garanti":[],"financement":["Temptraining"],"description_temptraining":"","description_caf":"","description_title":"Developing in Agentic AI Systems Training (GH-600)","objectifs":"<ul>\r\n<li>Position agentic AI in the software development lifecycle and describe the plan, act, evaluate cycle<\/li>\r\n<li>Design an agent architecture that separates planning, reasoning and execution<\/li>\r\n<li>Integrate agents into the SDLC with pull requests, CODEOWNERS, checks and protected environments<\/li>\r\n<li>Connect agents to tools through the Model Context Protocol and control their execution boundaries<\/li>\r\n<li>Orchestrate multiple agents with GitHub Actions workflows and isolate their execution<\/li>\r\n<li>Manage agent memory, state and context drift, then evaluate the quality of the work produced<\/li>\r\n<li>Apply risk-based governance with least privilege, human-in-the-loop review and full auditability<\/li>\r\n<\/ul>","connaissances":"<ul>\r\n \t<li>Practice with Git, branches and pull requests on GitHub<\/li>\r\n \t<li>Command of at least one programming language<\/li>\r\n \t<li>First use of GitHub Copilot or an equivalent coding assistant<\/li>\r\n<\/ul>","profils_participants":"<ul>\r\n<li>Developers who want to industrialize the use of coding agents<\/li>\r\n<li>DevOps and platform engineers responsible for GitHub Actions workflows<\/li>\r\n<li>Software architects designing automated delivery pipelines<\/li>\r\n<li>Technical team leads framing how AI is used on production code<\/li>\r\n<li>Application security engineers involved in repository governance<\/li>\r\n<\/ul>","examen":"<ul>\r\n \t<li>This course prepares you to the GH-600: GitHub Certified: Agentic AI Developer exam.<\/li>\r\n<\/ul>","documentation":"<ul>\r\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>\r\n\r\n<\/ul>","lab__exercices":"<ul>\r\n<li>This course provides you with exclusive access to the official Microsoft lab, enabling you to practice your skills in a professional environment.<\/li>\r\n\r\n<\/ul>","contenu_cours":"<strong>Module 1: Foundations of Agentic AI in GitHub<\/strong>\r\n<ul>\r\n \t<li>Define agentic AI in the SDLC<\/li>\r\n \t<li>Explain the agent lifecycle - plan, act, evaluate<\/li>\r\n \t<li>Describe GitHub as the system of record and control plane<\/li>\r\n \t<li>Identify responsibilities, risks, anti-patterns, and traceability needs<\/li>\r\n \t<li>Apply the contributor model to agent-generated work<\/li>\r\n<\/ul>\r\n<strong>Module 2: Designing Agent Architecture and SDLC Integration<\/strong>\r\n<ul>\r\n \t<li>Map agent responsibilities to the SDLC<\/li>\r\n \t<li>Define inputs, outputs, and success criteria<\/li>\r\n \t<li>Separate planning, reasoning, and execution<\/li>\r\n \t<li>Examples of implementing PR governance with templates, checks, CODEOWNERS, rules, and environment gates<\/li>\r\n \t<li>Build reliable workflows - outputs, contexts, triggers, and cross-job handoffs<\/li>\r\n \t<li>Control and operate agents - observability, tools, MCP, secrets, hooks, and reliability<\/li>\r\n<\/ul>\r\n<strong>Module 3: Tooling, MCP, and Agent Execution Environments<\/strong>\r\n<ul>\r\n \t<li>How agents interact with GitHub APIs and workflows<\/li>\r\n \t<li>Model Context Protocol (MCP) servers, registries, and allow lists<\/li>\r\n \t<li>Execution context and boundaries<\/li>\r\n \t<li>Agent execution limits and protections<\/li>\r\n<\/ul>\r\n<strong>Module 4: Multi-Agent systems and orchestration<\/strong>\r\n<ul>\r\n \t<li>Define multi-agent responsibilities in the SDLC<\/li>\r\n \t<li>Orchestrate agents using GitHub workflows<\/li>\r\n \t<li>Isolate execution - branches, workflows, permissions, and concurrency<\/li>\r\n \t<li>Detect and resolve conflicts using GitHub-native arbitration<\/li>\r\n \t<li>Make the system observable - attribution, evidence, and handoffs<\/li>\r\n \t<li>Operate reliably at scale - diagnose failures and recover safely<\/li>\r\n<\/ul>\r\n<strong>Module 5: Memory, State, and Evaluation<\/strong>\r\n<ul>\r\n \t<li>Implement agent memory strategies<\/li>\r\n \t<li>Persist agent state and manage context drift<\/li>\r\n \t<li>Ensure continuity of agent memory and state across tools and environments<\/li>\r\n \t<li>Define evaluation signals and enforce quality gates<\/li>\r\n \t<li>Analyze agent failures and improve behavior<\/li>\r\n<\/ul>\r\n<strong>Module 6: Governance, guardrails, and operations<\/strong>\r\n<ul>\r\n \t<li>Define risk-based autonomy and action boundaries<\/li>\r\n \t<li>Enforce governance with GitHub controls<\/li>\r\n \t<li>Design human-in-the-loop workflows<\/li>\r\n \t<li>Control agent capabilities using least privilege<\/li>\r\n \t<li>Make actions observable, traceable, and auditable<\/li>\r\n \t<li>Maintain governance and operational reliability<\/li>\r\n<\/ul>","cours_recommandes":[136552,136561],"infos_additionnelles":"<h3>Why take the Developing in Agentic AI Systems (GH-600) training<\/h3>\n<p>Coding agents moved from prototype to production pipeline in a matter of months. On GitHub, an agent opens a pull request, runs a test suite, fixes a defect, bumps a dependency. The question is no longer whether these agents write code, but how to frame their work so it stays traceable, reviewable and safe. Released by GitHub on 7 August 2026, GH-600 answers that need directly: designing agentic systems that rely on the platform native mechanisms rather than on improvised scripts.<\/p>\n\n<h3>GitHub as the control plane for agents<\/h3>\n<p>One idea runs through the official plan: GitHub is both the system of record for code and the control plane for agents. Repositories, branches, pull requests, status checks, CODEOWNERS and protected environments already form a proven framework for human work. The training shows how to apply that same framework to agent-generated work through the contributor model: an agent is a contributor like any other, whose contributions go through review, tests and merge rules. This approach avoids the most common trap, an agent writing to the repository through a parallel path outside any control.<\/p>\n\n<h3>Tooling, MCP and execution environments<\/h3>\n<p>An agent is only as useful as the tools it can reach. The Model Context Protocol module details MCP servers, registries and allow lists that decide what an agent may call. You work on execution context, its boundaries and the associated protections, in particular secret handling and token scoping. This is where many teams get it wrong: granting an agent too much reach is the same as granting an API token too much reach, with the same consequences.<\/p>\n\n<h3>Multi-agent orchestration with GitHub Actions<\/h3>\n<p>The day then moves to systems built from several agents: splitting responsibilities, orchestrating sequences with GitHub Actions workflows, isolating execution by branch and permission, handling concurrency, detecting conflicts and arbitrating them with GitHub-native mechanisms. The memory and state section completes the picture: memory strategies, context drift, continuity across tools and environments, evaluation signals and quality gates that show whether an agent improves or degrades across iterations.<\/p>\n\n<h3>Governance, guardrails and least privilege<\/h3>\n<p>The final module addresses governance: risk-based autonomy boundaries, human-in-the-loop workflows at sensitive steps, agent capabilities restricted by least privilege, actions that are observable, traceable and auditable. For a Swiss company subject to the revised Data Protection Act or to strong sector requirements, such as Geneva finance or healthcare, that traceability is not a nicety: it is the condition for allowing agents anywhere near production code. Knowing who decided what, on which basis and with which human validation becomes part of the compliance file.<\/p>\n\n<h3>The GitHub Certified: Agentic AI Developer certification<\/h3>\n<p>GH-600 prepares you for the GH-600 exam, which leads to the GitHub Certified: Agentic AI Developer certification. The official plan holds six modules delivered in a single day, which implies a fast pace and real prior comfort with Git, pull requests and GitHub Copilot. The day covers the exam scope; preparation then continues with practice on your own repositories, the surest way to anchor orchestration and guardrail concepts.<\/p>\n\n<h3>Where GH-600 sits among the ITTA GitHub trainings<\/h3>\n<p>The ITTA catalogue holds two neighbouring courses. The GitHub Copilot training (GH-300) covers a developer daily use of the assistant: suggestions, chat, test generation, refactoring. The GitHub Advanced Security training (GH-500) covers code scanning, secret detection and software supply chain security. GH-600 sits above both: it is not about using an assistant, but about designing a system where several agents work within a governed framework. GH-300 is a sound entry point, while GH-500 provides the security reflex that makes the final module guardrails far more concrete.<\/p>\n\n<h3>What the training adds over self-study<\/h3>\n<p>GitHub documentation on agents evolves quickly and reads easily. What it does not give you is the judgement call: which responsibility to hand to an agent, where to place human validation, how to isolate two agents touching the same repository, when to stop an agent stuck in a loop. A day with a certified instructor in Geneva or Lausanne lets you test those choices against real cases and avoid the anti-patterns the official plan flags from the very first module. Questions raised by other participants, often from teams of comparable size, are worth as much as the syllabus itself.<\/p>\n\n<h3>After the training<\/h3>\n<p>The natural next step is to pick a low-risk repository and deploy a first agent with a deliberately narrow scope: dependency updates, flaky test fixes, documentation generation. You then measure the evaluation signals seen in class before widening the scope. Documenting autonomy boundaries inside the repository itself, as rules and configuration files, keeps the framework alive after the person who built it moves on.<\/p>\n\n<h3>FAQ Developing in Agentic AI Systems (GH-600)<\/h3>\n<p><strong>Do I need programming experience for GH-600?<\/strong><\/p>\n<p>Yes. The course targets developers and DevOps engineers who already practise Git, pull requests and GitHub Actions. It does not revisit platform fundamentals.<\/p>\n\n<p><strong>How does it differ from the GitHub Copilot training (GH-300)?<\/strong><\/p>\n<p>GH-300 covers a developer daily use of the assistant. GH-600 covers the design, orchestration and governance of agent systems acting directly on the repository.<\/p>\n\n<p><strong>Is one day enough for six modules?<\/strong><\/p>\n<p>That is the official GitHub format. The pace is dense and assumes the stated prerequisites: without hands-on GitHub Actions experience, the orchestration part becomes hard to follow.<\/p>\n\n<p><strong>Does the course cover the Model Context Protocol?<\/strong><\/p>\n<p>Yes, the third module is dedicated to it: MCP servers, registries, allow lists, execution context and related protections.<\/p>\n\n<p><strong>How does the GH-600 exam work?<\/strong><\/p>\n<p>The exam is taken separately, with GitHub. The training covers the official plan and you remain free to schedule the exam whenever it suits you.<\/p>\n\n<p><strong>Should I take GH-500 before GH-600?<\/strong><\/p>\n<p>Not necessarily. GitHub Advanced Security (GH-500) remains a useful complement for teams that will have to justify the guardrails placed around their agents.<\/p>","inclus_dans_ce_cours_virtuel":"<ul>\r\n<li>Training provided by a certified trainer<\/li>\r\n<li>180 days of access to Official Microsoft Labs<\/li>\r\n<li>Official documentation in digital format<\/li>\r\n<li>Official Microsoft achievement badge<\/li>\r\n<\/ul>","inclus_dans_ce_cours_presentiel":"<ul>\r\n<li>Training provided by a certified trainer<\/li>\r\n<li>180 days of access to Official Microsoft Labs<\/li>\r\n<li>Official documentation in digital format<\/li>\r\n<li>Official Microsoft achievement badge<\/li>\r\n<\/ul>","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>GH-600 Agentic AI on GitHub Training - ITTA<\/title>\n<meta name=\"description\" content=\"GitHub Agentic AI training (GH-600): design and govern agents with GitHub Actions. 1 day in Geneva or Lausanne. 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