Run a fleet of AI coding agents in parallel — planning, building, reviewing, and shipping across your whole team.
One backlog feeds every agent. Planning, coding, review, and launch agents pull from it in the right order.
Connect every team member's computer as a worker. The orchestrator distributes work across available machines.
Route refactors to Codex, exploration to Claude Code, review to a reviewer agent, launch to the changelog flow.
Multi-agent software delivery is the architecture that emerges once a team adopts more than one AI coding tool. A planning agent breaks a feature request into a design; an implementation agent codes against the design; a reviewer agent reads the diff; a UI test agent verifies the change in a browser; a changelog agent drafts the launch copy. Each step is its own specialist, each runs on its own machine, and the orchestrator is the conductor. AI Expedite is the platform layer that makes the fleet behave like one team.
A single feature passes through five to seven specialist agents on its way from idea to shipped. The planning agent reads context and drafts the requirements. The design agent turns requirements into a technical design. The implementation agent (Claude Code or Codex) writes the code. The review agent checks the diff. The UI test agent verifies the behavior. The launch agent drafts the changelog and social copy. None of those steps is the whole job, and trying to make one model do all of them is how teams end up with brittle, low-quality output. AI Expedite specializes each step.
Each developer on the team installs a small terminal app and connects their machine to the workspace. From that point on, the machine is a worker the orchestrator can dispatch to — running Claude Code with the local subscription, running tests against the local toolchain, holding a working copy that's already authenticated against the team's GitHub. The orchestrator distributes work across whatever machines are online, so a team of five engineers running agents in parallel can ship five features at once without any one developer babysitting the queue.
Different agents are good at different things. Claude Code is strong at exploration and long-context reasoning; Codex is strong at batch and throughput; reviewer agents have to be conservative and tuned to your codebase. The orchestrator routes each task to the agent best suited for it: a 'refactor every async handler' job goes to Codex Batch; a 'add this new feature with tests' job goes to Claude Code; a 'review this PR' job goes to the reviewer. You don't configure the routing; you describe the work, and the orchestrator picks.
The hard part of running a fleet isn't dispatch — it's coordination. Two agents working on the same repo can't pick the same branch. A reviewer agent can't review a PR that hasn't merged yet. A launch agent can't draft a changelog from a feature that hasn't been verified. AI Expedite maintains the dependency graph between agent tasks and the ordering between them, so the system as a whole stays consistent. Failures roll back cleanly; successful runs cascade forward.
Yes. The terminal app is the worker; AI Expedite is the orchestrator. Your machines run the actual code and tests; we coordinate which machine takes which job. That's how subscription routing and local-only data stay possible.
They can't, by construction. Each agent task takes an exclusive branch and the orchestrator never assigns overlapping work. When two features touch the same file, the second one waits for the first to merge (or for a human to resolve the conflict in their IDE).
Only if you want both. The fleet works with whichever you have. Teams that pay for both get cost flexibility — the orchestrator routes each job to the cheaper viable agent.
Every agent task has a human approval gate by default at the requirements, design, and merge stages. You can relax those gates as the team builds trust with the fleet — some teams auto-approve through implementation and gate only at merge.
Running more Claude Code instances solves throughput, not coordination. Multi-agent delivery is about specialization (different agents for different steps), dependency tracking (which step waits for which), and routing (which agent gets which task). The 'more machines' part comes for free once the orchestration is in place.
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