[{"data":1,"prerenderedAt":817},["ShallowReactive",2],{"/topics/agentic-ai/agent-orchestration":3,"navigation-en-us":134,"banner-en-us":557,"footer-en-us":565,"next-steps-en-us":807},{"id":4,"title":5,"body":6,"category":6,"config":6,"content":7,"description":6,"extension":123,"meta":124,"navigation":125,"path":126,"seo":127,"slug":6,"stem":132,"testContent":6,"type":6,"__hash__":133},"pages/en-us/topics/agentic-ai/agent-orchestration/index.yml","",null,[8,22,28,99,101],{"type":9,"componentName":9,"componentContent":10},"CommonBreadcrumbs",{"crumbs":11},[12,16,20],{"title":13,"config":14},"Topics",{"href":15},"/topics/",{"title":17,"config":18},"Agentic AI",{"href":19},"/topics/agentic-ai/",{"title":21},"What is agent orchestration?",{"type":23,"componentName":23,"componentContent":24},"CommonArticleHero",{"title":21,"config":25,"text":27},{"id":26},"what-is-agent-orchestration","Agent orchestration is the structured coordination of multiple AI agents so they can work together across a workflow instead of acting in isolation. It routes work to the right agent, carries context forward, and applies the rules, permissions, and checkpoints that keep the overall process reliable. In software delivery, agent orchestration matters because modern work involves more than a single action. Planning, coding, code review, testing, security analysis, CI/CD, and remediation all happen across connected systems and stages, and orchestration coordinates those steps so teams can automate routine work without losing visibility or control.",{"type":29,"componentName":29,"componentContent":30},"CommonSideNavigationWithTree",{"anchors":31,"components":62},{"text":32,"data":33},"On this page",[34,38,42,46,50,54,58],{"config":35,"text":37},{"href":36},"#how-does-agent-orchestration-work","How does agent orchestration work?",{"config":39,"text":41},{"href":40},"#what-are-agents-and-flows","What are agents and flows?",{"config":43,"text":45},{"href":44},"#why-does-agent-orchestration-matter","Why does agent orchestration matter?",{"config":47,"text":49},{"href":48},"#what-are-the-main-agent-orchestration-models","What are the main agent orchestration models?",{"config":51,"text":53},{"href":52},"#what-does-agent-orchestration-look-like-in-software-delivery","What does agent orchestration look like in software delivery?",{"config":55,"text":57},{"href":56},"#how-does-gitlab-approach-agent-orchestration","How does GitLab approach agent orchestration?",{"config":59,"text":61},{"href":60},"#how-do-you-get-started-with-agent-orchestration","How do you get started with agent orchestration?",[63,69,74,79,84,89,94],{"type":64,"componentName":64,"componentContent":65},"TopicsCopy",{"header":37,"text":66,"config":67},"Agent orchestration assigns defined responsibilities to different agents, deciding how work moves between them, and preserving the state of the workflow as tasks progress. That means one agent can analyze a problem, another can take action, and another can validate the outcome, all without resetting context at every handoff.\n\nA well-orchestrated system usually includes:\n\n- Task routing, so the right agent handles the right step  \n- Shared context, so agents can build on earlier work  \n- Execution rules, so actions happen in the right order  \n- Escalation paths, so humans can step in when needed  \n- Monitoring, so teams can trace how the workflow ran\n\nTogether, these turn multiple AI agents into a usable system instead of a pile of disconnected tools.",{"id":68},"how-does-agent-orchestration-work",{"type":64,"componentName":64,"componentContent":70},{"header":41,"config":71,"text":73},{"id":72},"what-are-agents-and-flows","An agent is a specialized AI assistant designed for a specific type of work. In GitLab’s model, agents can support tasks such as planning, coding, security analysis, analytics, and other activities across the software development lifecycle.\n\nA flow is a multi-step workflow that combines one or more agents to solve a more complex problem. GitLab describes flows as guided sequences that automate manual steps, use project context throughout execution, and support reliable, repeatable outcomes across the software lifecycle.\n\nAgents are the specialists, and flows are the orchestration layer that tells them how to work together.",{"type":64,"componentName":64,"componentContent":75},{"header":45,"config":76,"text":78},{"id":77},"why-does-agent-orchestration-matter","Agent orchestration helps teams move beyond isolated AI assistance and toward coordinated automation. Without orchestration, teams can end up with multiple agents operating independently, overlapping responsibilities, or acting without enough context. With orchestration, work is structured, governed, and easier to scale.\n\nFor software teams, the biggest benefits usually include:\n\n- Fewer manual handoffs across lifecycle stages  \n- More consistent execution of repeatable workflows  \n- Clearer governance around where agents can act  \n- Better observability into what happened and why  \n- Faster progress on routine tasks so humans can focus on higher-value work\n\nThis is also why agent orchestration is increasingly tied to [agentic AI](https://about.gitlab.com/topics/agentic-ai/). Coordinating agents across real workflows matters as much as the capability of any single agent.",{"type":64,"componentName":64,"componentContent":80},{"header":49,"text":81,"config":82},"No single orchestration model fits every environment. Most teams choose between centralized, distributed, or hybrid coordination depending on how much control, flexibility, and oversight they need.\n\n### Centralized orchestration\n\nIn a centralized model, one control layer manages routing, approvals, and workflow progression. This is often easier to govern because execution paths and policy checks live in one place.\n\n### Distributed orchestration\n\nAgents coordinate more directly with one another. This supports flexibility in dynamic environments but adds more coordination complexity, because logic is spread across multiple systems or services.\n\n### Hybrid orchestration\n\nSome governance rules stay centralized while agents make local decisions inside those boundaries. This is a practical middle ground when organizations want both operational speed and policy control.",{"id":83},"what-are-the-main-agent-orchestration-models",{"type":64,"componentName":64,"componentContent":85},{"header":53,"text":86,"config":87},"In software delivery, agent orchestration is most useful when work spans multiple stages of the lifecycle. Instead of treating planning, development, security, and delivery as disconnected tasks, orchestration links them into a more continuous system.\n\nExamples of orchestrated software workflows include:\n\n- Turning planning inputs into structured work items  \n- Converting issues into implementation plans and merge requests  \n- Coordinating code review and feedback  \n- Diagnosing and fixing failing CI/CD pipelines  \n- Triaging and remediating security findings\n\nThis is where orchestration becomes a way to coordinate work across the full software lifecycle.",{"id":88},"what-does-agent-orchestration-look-like-in-software-delivery",{"type":64,"componentName":64,"componentContent":90},{"header":57,"config":91,"text":93},{"id":92},"how-does-gitlab-approach-agent-orchestration","GitLab positions GitLab Duo Agent Platform as an intelligent orchestration platform for DevSecOps, an AI-native solution that embeds multiple intelligent assistants throughout the software development lifecycle. Instead of a linear workflow, teams collaborate asynchronously with specialized agents across planning, coding, security, and delivery work.\n\nGitLab’s public platform emphasizes several core orchestration elements:\n\n- Specialized agents that use GitLab context  \n- Flows that guide agents through multi-step tasks  \n- Policy-driven control over where agents and flows can operate  \n- Traceability into how automation executed  \n- Extensibility so teams can adapt the platform to their workflows\n\nFor a product-oriented overview, GitLab’s [Duo Agent Platform public beta announcement](https://about.gitlab.com/blog/gitlab-duo-agent-platform-public-beta/) and [getting started guide](https://about.gitlab.com/blog/gitlab-duo-agent-platform-complete-getting-started-guide/) show how GitLab frames orchestration as many-to-many collaboration between developers and specialized agents across the software lifecycle.",{"type":64,"componentName":64,"componentContent":95},{"header":61,"text":96,"config":97},"The best way to start with agent orchestration is with a real workflow that creates friction: backlog analysis, code review, pipeline remediation, a repetitive security task. Then choose a multi-step workflow where orchestration can reduce manual coordination and improve consistency.\n\nA practical starting sequence looks like this:\n\n### 1\\. Define the workflow\n\nChoose the business or engineering process you want to improve and break it into steps.\n\n### 2\\. Assign agent responsibilities\n\nDecide which tasks belong to which agent and where human review is still required.\n\n### 3\\. Set the rules\n\nDefine triggers, permissions, checkpoints, and escalation paths so the workflow runs consistently.\n\n### 4\\. Monitor the workflow\n\nTrack how the orchestration runs, where it pauses, and what needs refinement before expanding it further.\n\n### 5\\. Expand intentionally\n\nOnce one orchestrated workflow is working well, extend the model to adjacent use cases instead of piling on agents without coordination.",{"id":98},"how-do-you-get-started-with-agent-orchestration",{"type":100,"componentName":100},"CommonNextSteps",{"type":102,"componentName":102,"componentContent":103},"CommonFaq",{"header":104,"groups":105},"Frequently Asked Questions",[106],{"questions":107},[108,111,114,117,120],{"question":109,"answer":110},"What is agent orchestration in simple terms?","Agent orchestration is the coordination layer that helps multiple AI agents work together across a workflow with shared rules, context, and checkpoints.",{"question":112,"answer":113},"What is the difference between an agent and a flow?","An agent is a specialized AI assistant for a specific task. A flow is a multi-step workflow that combines one or more agents to solve a more complex problem.",{"question":115,"answer":116},"Why is agent orchestration important for software teams?","It helps software teams coordinate planning, development, review, CI/CD, and security work more reliably by reducing manual handoffs and making automation easier to govern and observe.",{"question":118,"answer":119},"What are the benefits of agent orchestration?","The main benefits are better workflow coordination, stronger governance, clearer traceability, and a more scalable way to use AI across multiple tasks and teams.\n",{"question":121,"answer":122},"How does GitLab support agent orchestration?","GitLab supports agent orchestration through GitLab Duo Agent Platform, which combines specialized agents, multi-step flows, policy controls, traceability, and lifecycle context across DevSecOps 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