AI agent teams explained

An AI agent team is a group of AI workers that each hold one job, such as planning, designing, building or testing, and pass the work along in order. This article explains who is on the team, what each one hands over, and what you do.

• An AI agent team splits one big software job into roles, so each agent keeps a narrow focus and a smaller context.
• Checks happen between roles: the next agent reads what the last one made before it builds on it.
• A typical team has a product manager, an architect, a designer, a developer, QA, security and release.
• You describe the project, answer questions, approve tickets and check the work.
• A team uses more tokens than a single chat, and ULU shows the cost of every step.

What an AI agent team is

An AI agent is a program that uses a language model to do a task, read files, run tools and decide what to do next. One agent can do a lot. But when you ask it to plan, design, build, test and ship a whole piece of software, it has to hold all of that in its head at once.

An AI agent team takes the other route. It gives each job to its own agent, with its own instructions, and lets the agents work one after another. This is AI agents working together the way a small company does.

In ULU, a desktop app for Claude Code, each agent is a bee. You describe your project in plain words, and Kukulu, the builder bee, turns the description into a team and shows you every step while the bees work. If you want the bigger picture, read what an AI software factory is.

Why roles beat one general agent

A multi-agent system for software is not magic. It works for three plain reasons.

Focus

An agent told to be a product manager only thinks about what to build and why. It is not tempted to start writing code halfway through. An agent told to be QA only tries to break things. A narrow job gives clearer instructions, and clear instructions give steadier results.

A smaller context each

Every agent has a context window: the amount of text it can keep in mind at one time. When one agent does everything, that window fills with plans, code, test output and old decisions. Once it is nearly full, the agent gets worse at the start of its own work.

On a team, each agent reads only what its role needs, such as the written plan and the files it must touch. ULU shows how full each agent's context is, and a bee looks tired when its memory is nearly full, so you see the problem before it costs you.

Checks between roles

When one agent writes the code and the same agent says it works, nobody has checked anything. On a team, the work crosses a boundary. QA runs the tests that the developer did not write. Security reads the code that the developer left behind. A second pair of eyes catches mistakes the first pair is blind to.

The roles and what each hands to the next

The exact team depends on the project. ULU can design a custom team from your description, or you can start from a ready one for your tech stack. See the ten ready teams. The roles below are the usual ones.

The usual roles in an AI agent team, what each produces and what each checks

Role	What it produces	What it checks
Product manager	A written plan of what to build, with clear success tests	That the request is clear, in scope and testable
Architect (tech lead)	A design of how the parts fit together	That the plan can be built and the finished code matches the design
Designer	The screens and the look of the product	That the flow makes sense for the person using it
Developer	The code, in the project folder	That the code builds and follows the design
QA	Tests and test results	That the product does what the plan says, including odd cases
Security	A list of problems found, with how serious each is	Secrets, risky code and weak spots
Release	The built product, ready to ship	That the earlier steps passed before it ships

The AI product manager agent

An AI product manager agent is the start of the chain. It takes your idea and writes down what to build in words the rest of the team can follow. It also decides what is not part of the job. Everything after it leans on this page, so a vague plan here becomes a wrong product later. That is why ULU asks you questions before the work starts.

The architect, designer and developer

The architect decides how to build it: which parts exist and how they talk to each other. The designer decides how it looks and how a person moves through it. Both hand a written result to the developer, who writes the code from the plan, the design and the screens, so it does not guess.

The AI QA agent

An AI QA agent runs the project's own tests and looks for cases the developer missed. ULU runs the test suites that a project has, such as unit, integration and end-to-end, and shows the counts as a pyramid. If a build check finds a leftover placeholder or a missing file, ULU asks Claude to repair it.

Security and release

Security looks for problems that tests do not catch. Release prepares what ships. In ULU, the team stops for your approval on the design and on the release, so nothing goes out without you.

How the orchestrator passes work along

The agents do not chat with each other at random. One agent, the orchestrator, runs the order. In ULU it is El Capitan, the orchestrator bee.

El Capitan reads the request and hands out the work. The product manager writes down what to build. The tech lead decides how. The developer writes the code. QA checks that it works. Each step starts when the one before it is done.

You can watch this happen. In ULU every agent sits in its own cell of a honeycomb. When El Capitan hands out work, a gold pulse moves from cell to cell, and the bee that receives it starts to buzz. You can see the hive at work instead of reading a wall of log lines.

What you do

You are not a bystander, and you are not the coder. Your part has four jobs.

• Describe. Tell Kukulu what you want in your own words. It writes a project brief and asks only about what is missing.
• Answer questions. The bees ask when they need a decision. Your answers shape the result more than any setting does.
• Approve tickets. Teams can work from an inbox of tickets. Nothing in the inbox runs until you approve it. Teams can also run on a schedule, for example at night.
• Check the work. You review the design and the release, and you can ask the team for a health report or describe a change at any time.

You can do this away from the desk. With ULU Remote, you pair a phone by QR code, or you connect a Telegram bot. You can then follow builds, review designs, answer the bees' questions and approve or decline a ticket. The phone and Telegram see titles and names, never commands or files.

If you have never used Claude Code and the terminal worries you, read Claude Code for people who don't code. The steps are laid out on the page about how it works.

The honest part: a team costs more

A team of agents uses more tokens than one chat. Several agents each read the project and each write their own output, and the checks between them are extra work. If you ask one chat for a small script, a team is more than you need.

What you get for the extra cost is structure: a plan, a design, tests and a review. Whether that is worth it depends on the project. For a small change, probably not. For something you want to keep and grow, it often is.

Agents run on your own Claude plan or API key, so the money is real. ULU shows the running cost in US dollars for every step and in total, plus the time and the files written. A build in progress might show a step of a few minutes costing a few dollars, and a total beside it. Those figures in the app are examples, not a promise of what your project will cost. For a longer look at the money, read what AI agents cost, and see how ULU tracks it.

Where to start

If you already know your stack, a ready AI team for your tech stack is the shortest way in. If you do not, describe the project and let Kukulu propose a team. You see it as a honeycomb, change what you like and approve it.

Questions people ask

What is an AI agent team?

It is a group of AI agents, each with one role, that work on a task one after another. A common team has a product manager, an architect, a designer, a developer, QA, security and release. An orchestrator decides the order and passes the work from one to the next.

Is a team better than one AI agent?

It depends on the job. For a small, simple task, one agent is faster and cheaper. For a whole project, a team gives each agent a narrow focus and a smaller context, and it puts checks between the roles. It also costs more tokens.

Do I need to know how to code to use an AI agent team?

No. In ULU you describe the project in plain language and answer questions. You approve a picture of the team, not a folder of configuration files. You do need your own Claude plan or API key.

What does an AI QA agent actually do?

It runs the tests and looks for cases the developer did not cover. In ULU, the project's own unit, integration and end-to-end tests are run and the counts are shown as a pyramid. A failed check, such as a leftover placeholder or a missing file, is sent back to Claude to repair.

How do I know what the team is costing me?

ULU shows the cost in US dollars for each step and in total, along with the time and how full each agent's context is. You can stop a build at any point and continue later, even days later.

Can the team work while I am away?

Yes. A team can run on a schedule, such as at night, and it works from tickets that you approve. You can follow it and answer questions from your phone with ULU Remote or Telegram.

Try it

ULU 0.7.0 is out for Mac and Linux, and Windows is in the works. Your first project is free. Install ULU, describe your project and watch the team work.