How to coordinate multiple AI agents so they stop stepping on each other

To coordinate multiple AI agents, give the work one coordinator, give every task exactly one owner, and give each agent its own isolated workspace. Keep shared context in files every agent can read, route every question for you into one approvals queue, and put all scheduled work on one visible calendar.

Why do multiple AI agents get in each other's way?

Agents collide for the same reasons people do on a team with no manager. Once two or more run at the same time, the same five problems keep showing up:

1. Two agents edit the same files. One rewrites a module while the other is halfway through changing it, and someone's work is lost or the merge breaks.
2. Nobody owns a task. You assume an agent picked it up. The agent assumed it was already done.
3. Work gets duplicated. Two agents research the same question, or fix the same bug in two different ways.
4. Context is lost between agents. The agent writing the landing page never heard about the pricing decision another agent made yesterday.
5. An agent waits on you without telling you. A permission prompt sits in a terminal tab you closed an hour ago.

Every one of these is a coordination problem, and by default you are the coordinator: switching windows, carrying state in your head, and noticing collisions after they happen. The fixes below move that job out of your head and into structure that works with any agent tool.

Do I need a coordinator agent to manage my other agents?

Yes, once you run more than two or three agents at a time. A coordinator is the one place where work is planned, split and assigned. You talk to it, and it talks to the workers.

The coordinator has three jobs:

• Plan. Turn your goal into tasks with clear done criteria.
• Assign. Give each task to one worker suited to it, such as a coding agent for the API change and a writing agent for the launch post.
• Track. Know what is in progress, what is blocked and what has been delivered.

Keep the coordinator out of the hands-on work. A coordinator that also writes the code fills its context with implementation detail and loses the big picture. In Claude Code, a lead session that delegates to subagents and keeps the plan in a plan.md file is a workable version of this pattern. Writing the plan to a file matters, because a plan that lives only in a chat ends with the session.

For a closer look at this pattern, read how Claude can manage other agents.

How do I make sure every agent task has exactly one owner?

Give every task a written record with four fields: owner, status, done criteria and deliverable. Then follow three rules:

1. One task, one owner. If two agents need to touch something, split it into two tasks with a clear handoff between them.
2. Claim before starting. An agent sets itself as owner and moves the task to in progress before doing any work. A task with no owner has not started, whatever you remember asking for.
3. Close with a deliverable. A task is done when its deliverable exists where the record says it should: a merged pull request, a file in reports/, a published link.

The format can be simple. A markdown table in the repo works for a weekend project. GitHub issues with one label per agent work for a busier one. What matters is that the record lives outside any single agent's conversation, so every agent, and you, read the same list.

Claiming before starting is also what stops duplicated work. An agent that checks the list first sees the task is taken and picks another.

How do I stop two AI agents from editing the same files?

Give each agent its own isolated workspace, and let their changes meet only through review.

For code, the lightest version is a git worktree per agent. Each worktree is a separate checkout on its own branch, backed by one repository:

git worktree add ../app-billing -b agent/billing
git worktree add ../app-onboarding -b agent/onboarding

Each agent works in its own folder, and changes merge through pull requests, where conflicts show up as diffs you can read.

Watch for what a worktree leaves shared:

• Ports. Two dev servers both want port 3000.
• Databases and test data. Two agents running migrations against one local database can wreck each other's state.
• Environment files. A .env file is usually untracked, so a new worktree starts without it.
• Dependencies. Each worktree needs its own install.

A separate container or cloud machine per agent removes most of these at once, at the cost of setting them up. Either way, write ownership boundaries into the brief, for example "the billing agent owns src/billing and edits nothing outside it."

How do AI agents share context without overwriting each other?

Share context for reading, and keep writing scoped to each agent's own task. Agents need the same background, but each one should only change what it owns.

Three practical layers:

• Project memory files. CLAUDE.md for Claude Code and AGENTS.md for Codex hold the stack, the test commands and the conventions every agent follows. Change them deliberately, between tasks.
• A decisions log. One file where any decision that affects other work gets recorded, for example "the trial is 14 days" or "we use Postgres." Agents read it before starting and append to it when they decide something.
• Handoff notes. Each agent ends its task with a short summary of what it did, what it decided and what the next agent should know, saved next to the task record.

Credentials are context too. Connect each service once, somewhere every agent can reach. A token pasted into each session ends up in each transcript.

The test is simple: could a fresh agent pick up any task from the record and the files alone, without reading another agent's chat? If it could, your context is shared properly.

How do I know when an AI agent is waiting on me?

Route every question from every agent into one queue, and decide in advance what is allowed to ask.

Agents need you for a short list of things: signing in, connecting an account, choosing between options, and approving anything public or hard to undo, like a post or a deploy. Write that list down. Everything outside it should run without asking, which usually means allowing safe actions up front, such as running tests or editing files inside the agent's own workspace.

Then make the asks visible in one place:

• Notifications. Claude Code hooks can run a command when Claude needs input or finishes, and Codex has a notify setting that runs a program when a turn completes. Send both to the same desktop or phone channel.
• One queue. If you check five terminals for prompts, you will miss one. A single list of open asks can be cleared in one pass.
• Park blocked work. A blocked task should wait for you while the agent moves on to something else.

A lot of the friction of managing many agent sessions comes down to this one problem.

How do I see what all my AI agents are doing this week?

Put every scheduled and in-progress job on one calendar-style view. Cron jobs, CI schedules and reminders spread across machines hide your agent work, and hidden work collides.

A visible schedule answers questions you otherwise learn the hard way:

• Is the weekly dependency update running at the same time as the release build?
• Which tasks are due this week, and which have slipped?
• What ran overnight, and did it deliver?
• Which agent is free to take something new?

The simplest version is a shared calendar where each recurring job is a repeating event and each task with a deadline has a due date. A schedule file in the repo also works, as long as you and every agent read the same one.

Give multi-day work a start and an end, so it shows as a band across the days it occupies. Then look at it once a week: delete routines nobody reads, and move apart jobs that compete for the same resources.

How does Kav coordinate worker agents in Kavela?

In Kavela the coordinator is built in. You talk to one agent, Kav, who runs your workspace: Kav plans the work, files tasks and hands each part to the right worker agent.

• One owner per task. Kav hands each task to one worker, and every task, routine and event lands on the workspace calendar (month, week and day views), so you see what is running and when.
• Isolation. Each worker gets its own cloud computer with a real shell, files, git and a browser. Runs keep going after you close the tab.
• Shared context. Connections belong to the workspace: connect GitHub, Gmail or Stripe once and every agent can use it. Claude Code and Codex workers still get the workspace's files, skills and connected apps through a run-scoped tool channel.
• One queue. Anything that needs you waits in one Approvals inbox. Runs park and resume when you answer.
• Away from your desk. Talk to Kav from Telegram, answer approvals there, check /tasks and get a regular digest.

Each task ends with its deliverables attached, so review happens in one place. Agent packs, like Engineering Agents with Ship and Echo, add a ready-made team in one click.

Kavela is in early access. Start your free trial and let Kav run the coordination.