Agent orchestration for software teams: patterns and 9 platforms compared
Published October 2, 2026
Summary
Agent orchestration coordinates several AI agents toward one goal. For software teams, that means deciding which agent takes which task, where each one runs, and how its work is checked and merged. This guide covers the patterns and compares nine platforms that orchestrate coding agents.
Definition
What is agent orchestration?
Agent orchestration is the layer that coordinates multiple AI agents. It routes work to the right agent, carries context between steps, enforces limits, and decides when a person has to approve something. GitHub describes it as a control layer for managing execution, context, and collaboration across agents.
The term covers two different jobs, and most search results mix them up.
- Building multi-agent applications
- Frameworks such as LangGraph, CrewAI, and the OpenAI Agents SDK let developers define agents, tools, and handoffs inside their own product, such as a support bot that routes between a billing agent and a refunds agent.
- Orchestrating coding agents for a software team
- Platforms that take coding agents such as Claude Code, Codex, or Devin, give each one a task and an environment, and bring the results back for review. This guide covers the second job.
Patterns
Four orchestration patterns for coding agents
Most setups combine these patterns. Choosing among them decides how much work runs at once and where people step in.
- Fan-out
- One large job, such as a migration or a dependency upgrade, is split into independent tasks that run in parallel, each producing its own pull request.
- Pipeline
- Work passes between steps: one agent writes a spec, another implements it, another reviews the diff, and CI decides whether it can merge.
- Event-driven
- Agents start from events instead of prompts: a new issue, a failed build, an error alert, a review comment, or a schedule.
- Supervisor
- A lead agent splits a task into subtasks, delegates them to subagents, and combines the results. Claude Code subagents work this way inside one session.
- Whatever the pattern, decide where a person approves: the plan, the pull request, the merge, or the deploy.
What it takes
What an orchestration layer has to provide
Running one coding agent is easy. Running many at once raises the same questions every time.
- Task intake
- A shared queue fed by the places work already starts, with clear boundaries so two agents do not take the same task.
- Isolation
- Each agent needs its own checkout and, ideally, its own runtime, so tests, ports, and dependencies do not collide. The multi-agent orchestration guide compares worktrees, containers, and VMs.
- Environment setup
- Agents need the repository, dependencies, services, and credentials prepared the same way every time.
- Agent and model choice
- Whether the platform runs one vendor's agent or lets you pick Claude Code, Codex, or another agent per task.
- Verification and review
- Tests, CI, and a reviewer other than the agent that wrote the change, plus a way for the agent to continue from feedback.
- Visibility and cost
- A view of what every agent is doing, who started it, and what it cost.
Nine platforms
Coding agent orchestration platforms compared
These products coordinate coding agents for a team. They differ in which agents they run, where those agents run, and what starts the work. This category changes quickly, so confirm details with each vendor.
| Platform | Agents it orchestrates | Where agents run | What starts the work | Best for |
|---|---|---|---|---|
| Replicas | Claude Code, Codex, Cursor, OpenCode, and other supported agents | One Linux VM per task | Slack, Linear, GitHub, GitLab, schedules, webhooks, CI failures, API | Teams orchestrating the agents they already use from team tools |
| Claude Code | Claude Code and its subagents | Local worktrees or Anthropic-hosted cloud sessions | Terminal, web, and routines on schedules, API calls, or GitHub events | Teams standardized on Anthropic |
| Cursor Cloud Agents | Cursor Agent | Isolated VMs | IDE, web, mobile, Slack, GitHub, Linear, API | Teams standardized on the Cursor editor |
| GitHub Copilot cloud agent | Copilot | Ephemeral GitHub Actions environments | GitHub issues and pull requests | Organizations that live entirely in GitHub |
| Devin | Devin, with MultiDevin for parallel teams of agents | Managed Devin sessions; VPC deployment for enterprise | Devin app, Slack, Linear, GitHub | Teams that want one packaged agent at scale |
| Factory | Droids, including multi-agent Missions | Local machines or managed Droid Computers | CLI, desktop, web, Slack, Microsoft Teams, Jira, Linear, CI | Organizations standardizing on Droids |
| Warp | Warp Agent; Warp says teams can bring harnesses such as Claude Code or Codex | Warp infrastructure or self-hosted | Slack, Linear, GitHub, webhooks, schedules | Terminal-first teams adopting cloud agents |
| OpenHands | OpenHands, plus ACP-compatible agents such as Claude Code and Codex in Agent Canvas | Local, OpenHands Cloud, or self-hosted | GUI, CLI, SDK, Slack, Jira | Teams that want open-source orchestration they can host |
| Conductor | Claude Code, Codex, and Cursor | Isolated workspaces on your Mac; cloud workspaces on paid plans | The Conductor app | Developers running parallel agent sessions |
Replicas: best for orchestrating the agents your team already uses
Replicas takes tasks from Slack, Linear, GitHub, GitLab, schedules, webhooks, failed CI runs, and the API, and runs each one in its own Linux VM prepared from an environment the team defines once. Teams choose Claude Code, Codex, Cursor, OpenCode, or another supported agent per task, so one queue and one environment serve every agent.
Reviewers can watch any session, take over the desktop or browser, comment on the diff, and have the agent continue in the same workspace. Organization analytics show which agents ran, from which trigger, who started them, and what they cost.
Claude Code: best for orchestration inside one provider
Claude Code splits work across subagents, can run them in separate worktrees, and its /batch command fans a migration out to many worktree agents that each open a pull request. Anthropic-hosted cloud sessions run in parallel, and routines, in research preview, start work from schedules, API calls, or GitHub events. Everything runs Claude Code.
Cursor Cloud Agents: best for teams standardized on Cursor
Cloud Agents run in isolated VMs and start from the editor, web, mobile, Slack, GitHub, Linear, or an API. Each returns a branch and pull request. Cloud work runs through Cursor Agent rather than external agents.
GitHub Copilot cloud agent: best for GitHub-only organizations
Assign issues to Copilot and each one runs in a GitHub Actions environment and returns a pull request. GitHub describes Agent HQ's mission control as its direction for managing agents. Everything stays inside repositories hosted on GitHub.
Devin: best for scaling one packaged agent
Devin runs tasks in its own managed sessions, and MultiDevin manages parallel teams of Devin agents. It suits organizations that want one vendor to own the agent, the environment, and the workflow.
Factory: best for organizations standardizing on Droids
Factory coordinates Droids across implementation, review, QA, and documentation, including reusable multi-agent Missions. Models are configurable, but every step runs as a Droid.
Warp: best for terminal-first teams
Warp's cloud agents start from Slack, Linear, GitHub, webhooks, or schedules and can run on Warp's infrastructure or your own. Factories, its layer for repeatable agent workflows with human approval steps, is in early access.
OpenHands: best for open-source orchestration you can host
OpenHands is open source and model-agnostic. Its Agent Canvas coordinates parallel sessions in isolated worktrees and can connect ACP-compatible agents such as Claude Code and Codex. Self-hosting puts operations on your platform team.
Conductor: best for parallel sessions on your own machine
Conductor is a Mac app that runs Claude Code, Codex, and Cursor agents in parallel isolated workspaces, with a view of what each one is doing and a path to review and merge. Cloud workspaces are available on paid plans.
Decision guide
How to choose an orchestration platform
Start with scale. One engineer running a few agents is well served by worktrees and the orchestration built into Claude Code or Conductor. A team delegating work from its backlog needs a shared queue, shared environments, and review everyone can see.
Decide whether agent choice matters. Devin, Factory, Cursor, and Copilot orchestrate their own agent. Replicas, OpenHands, and Conductor can run several third-party agents. If engineers already prefer different agents for different work, pick a platform that does not force one.
Map what starts the work. Event-driven orchestration only covers the events a platform can see, so check your issue tracker, chat, source control, CI, and alerting against each product.
Plan for review. Orchestration makes it easy to produce more pull requests than a team can review, so measure what your reviewers can handle before adding agents.
FAQ
Agent orchestration questions
Getting started with Replicas
Orchestrate your first agents without building the platform
Replicas includes a 14-day free trial with no credit card required. Connect a repository, define the environment once, and assign a few independent tickets from the dashboard, Slack, or Linear. Each task runs in its own Linux VM with the agent you pick, and comes back as a pull request for your team to review.