What is multi-agent development?
Multi-agent development is a way of building software in which several AI coding agents work on the same codebase at the same time, each with a defined job, and their results are reviewed and combined. The agents can be copies of one CLI or a mix, such as Claude Code building while Codex reviews.
It is not the same as one agent that spawns helpers internally. In multi-agent development each agent is a full session with its own workspace, its own terminal and its own diff. You can see, stop, question and review each one.
Why use more than one AI agent?
You use more than one agent to get three things a single session cannot give you: throughput, a second opinion and separation of roles.
- Throughput — one agent is sequential. Five agents on five independent tasks finish five tasks while you would have finished one.
- A second opinion — different agents make different mistakes. When three attempts agree, the answer is probably right. When they split, you have found the hard part of the problem.
- Separation of roles — an agent that reviews code it did not write catches things the author skips. A builder and a reviewer in a loop is closer to how human teams work.
For a direct comparison of the two ways of working, read one agent vs many.
What are the main multi-agent development patterns?
Most multi-agent work fits one of four patterns. They differ in whether agents share the same objective and in who combines the results.
| Pattern | How it works | Best for | In Tallos |
|---|---|---|---|
| Parallel independent tasks | Different agents take different tasks at once | Backlogs of unrelated fixes and features | One worktree per task, any agent |
| Best-of-N | Several agents attempt the same objective; you keep the best | Performance work, hard bugs, open design questions | Squad template “3 attempts, pick the best” |
| Build + review | One agent builds, another reviews; loop until approved | Refactors, migrations, risky changes | Squad template “Build + review” |
| Leader divides the work | A leader splits the objective, assigns parts, combines results | Features that span API, UI and tests; test sweeps | Squad template “Leader divides the work” |
- 1
Parallel independent tasks
The simplest pattern. You open a workspace per task and start an agent in each. Nothing is shared, so nothing conflicts. You review each diff on its own. See run AI agents in parallel.
- 2
Best-of-N
The same prompt goes to several agents, ideally different ones. Each works in its own worktree. You or a leader compare correctness, tests and diff size, and keep one. The others stay available until you delete them. See the best-of-three squad.
- 3
Build + review
A builder implements; a reviewer lists what must change; the builder fixes; repeat until the reviewer approves or the round limit is reached. You see the result only after it has passed review. See the build + review squad.
- 4
Leader divides the work
A leader agent reads the objective, splits it into independent parts, gives one to each worker, checks each result and combines everything into one final result.
Where should I start with multi-agent development?
Start with parallel independent tasks, then try best-of-N on one hard problem. Both need nothing more than separate workspaces. Move to build + review and leader-divides once you trust the loop.
Build your first squad in Tallos: pick a template, choose the agents, set the limits.
What goes wrong when several agents work on one codebase?
Without structure, multiple agents create coordination problems faster than they create code. These are the failure modes to plan for.
- File conflicts — two agents in the same folder overwrite each other's edits, and neither notices.
- Branch confusion — agents switch or commit to the wrong branch, and one task's changes leak into another.
- Terminal sprawl — agents, dev servers and test runs across many windows; you lose track of which is which.
- Split context — each agent only knows its own prompt. Without a clear division of work, two agents solve the same part twice or make incompatible choices.
- Silent blocking — an agent waits on a question for an hour because its terminal was hidden.
- Review overload — five agents produce five diffs. If review is not built in, it gets skipped, and unreviewed code ships.
- Runaway loops — an orchestrating agent can keep dispatching work and spending usage with no end point.
How do isolated workspaces solve agent conflicts?
Isolated workspaces solve conflicts by giving each agent its own copy of the files and its own branch, so parallel edits never touch the same checkout. Git worktrees do this cheaply: several working directories share one repository, each on a different branch.
In Tallos every task is a real git worktree with its own branch, files on disk, terminals and browser tabs. Two agents can rewrite the same file in two worktrees, and you decide at review time which change survives. Conflicts move from “agents fighting in one folder” to a normal merge you control. Details are in parallel workspaces.
Isolation also fixes terminal sprawl and silent blocking. Terminals are scoped to their workspace, and each agent session shows whether it is working, done or waiting on you.
How do squads coordinate a team of agents?
A squad is a leader agent plus member agents that you define, with limits you set and a review at the end. The leader plans the work, hands out tasks, checks the results and reports back. It is how Tallos handles the split-context, runaway-loop and review-overload problems.
- 1
Write the objective
Describe the result in plain words. Optionally paste a GitHub, GitLab, Linear or Jira link as the task source.
- 2
Pick a template
Build + review, 3 attempts and pick the best, Leader divides the work, or Custom. Templates only prefill the team; you can edit everything.
- 3
Set the team
Choose the leader's agent and each member's agent, model and effort level, describe each member's job, and set how many copies of a member to run.
- 4
Set the limits
Max rounds and max members working at once. Defaults are 5 rounds and 4 members; the hard maximum is 20 rounds and 12 members. The leader cannot exceed them.
- 5
Let it run
Each round the leader plans, dispatches members into their own worktrees and reviews what comes back. If it is truly blocked, it asks you a question with options, and you answer from the squad view.
- 6
Review and decide
The leader writes a final report and recommends a result. You see results by worktree, open the changes, then Accept or Discard. Nothing is merged until you decide.
Squads can be saved, reused, duplicated and scheduled hourly, daily, on weekdays, weekly or on a custom schedule. Run history shows each run's rounds, started, succeeded and failed members, and an estimated cost. Read more on the squads feature page.
Give an objective to a leader agent and review the result. Squads run on macOS and Windows.
When is one agent enough?
One agent is enough when the task is small, tightly coupled or needs constant back-and-forth with you. Splitting a one-file change across three agents adds review work without adding value. Multi-agent development pays off on independent tasks, on problems with several plausible solutions, and on changes risky enough to deserve a second agent's review.
Ready to try it? Follow your first squad step by step.
Frequently asked questions
What is a multi-agent coding workflow?
A multi-agent coding workflow runs several AI coding agents on one codebase at the same time, each with its own task or attempt and its own workspace, and then reviews and combines their results.
Can I mix Claude Code, Codex and Gemini CLI in one team?
Yes. In a Tallos squad the leader and each member pick their own agent, model and effort level, so a Claude Code leader can direct Codex and Gemini CLI members.
How do I stop multiple AI agents from overwriting each other's code?
Give each agent its own git worktree. Tallos creates one per task and one per squad member, so agents never share a working directory and you merge only what you accept.
What is best-of-N in AI coding?
Best-of-N sends the same objective to several agents, lets them work independently, and keeps the best result. Tallos ships it as the squad template “3 attempts, pick the best.”
How many agents can a Tallos squad run?
By default a squad runs up to 4 members at once for up to 5 rounds. You can raise the limits to at most 12 members and 20 rounds.
Does the leader agent merge code on its own?
No. When the squad finishes, the leader writes a report and recommends a result, and you accept or discard it. Discarding keeps or deletes the members' worktrees, your choice.
Does multi-agent development cost more?
Each agent uses your own subscription or API key, so running more agents uses more of your allowance. Squad run history shows an estimated cost for each run.
