How do I run multiple AI coding agents at once?
Why parallel agents break (and it isn't the model)
One agent in a terminal is easy. Ten across different branches and PRs means manually creating branches, checking which agent is stuck, reading CI failures, forwarding review comments, and tracking which PRs are ready. The bottleneck isn't agent capability — it's coordination and prioritization. Uncoordinated agents are a team with no project plan: they duplicate work and conflict on shared files.
The pattern that works
1. One shared queue, always prioritized — every agent asks "what's the most valuable thing to do right now?" and gets one answer.
2. Leases — when an agent takes a task it's locked, so the others skip it and the fleet self-balances.
3. A never-stop loop — agents keep pulling until the queue is empty; the system finds the next valuable work itself.
4. Human only on hard gates — spending money, shipping to prod, anything irreversible.
TaskPeace is built around exactly this loop — it's MCP-native, so it drops into Claude Code, Cursor, or Codex in one line.
Get early access → Read the fleet guide
FAQ
- How many AI agents can I run at once?
- As many as your queue and rate limits allow. With a prioritized queue + leases, agents self-divide the work, so going from 1 to 10+ adds throughput instead of chaos.
- Do I need a framework like CrewAI or LangGraph?
- Those wire agents together into a graph. Running a fleet against a shared prioritized queue is a different, simpler model — it decides what each agent does next and keeps them coordinated, and works with the agents you already use.
- How do agents avoid doing the same task twice?
- A lease: when an agent pulls a task it is marked in-progress, so every other agent skips it. If an agent dies, the lease expires and the task returns to the queue.