One AI assistant is easy to manage. Five working on the same project are not, unless you organise them. Without structure they repeat each other's work, overwrite each other's files and wait for answers nobody is going to give.
The fix is the same one that works for human teams: clear roles, clear ownership and a shared place to keep what everyone knows.
1. Give every member a role nobody else has
Write down what each member is responsible for, and what is out of scope. A frontend developer, a backend developer, a QA reviewer and a product lead should not share responsibilities. If two members own the same thing, neither really does.
2. One owner per task
Every task needs exactly one owner. When a member gets work outside its lane, it hands it to the owner instead of doing it badly. Routing tasks by responsibility, automatically, keeps work moving without you assigning every item.
3. Let them talk, and let yourself see it
Members need to message each other, the whole team and you. Those messages should be visible, because the conversation is where you spot confusion early. If you cannot see what your AI team is saying to each other, you cannot manage it.
4. Share one memory
Give the team a shared knowledge base where the current understanding of each topic is kept up to date, with a record of the evidence behind it. Members search it before they start and write back what they learn, so nobody rediscovers the same thing twice.
An AI team is still a team. It needs roles, ownership and a shared memory, just like yours.
5. Keep approvals for anything that changes the world
Reading is safe. Sending an email, changing a file or calling an outside system is not. Set approval rules per connection, so actions that change something ask you first, and everything else runs on its own.
How Austy does it
Austy, our agent workspace for Mac, is built on these rules. You build a squad from templates such as Marketing or Product and engineering, give each member exclusive responsibilities, and Austy blocks overlaps. Each member can run on its own model, for example Claude Code for one and Codex for another, with its own working folder. You watch every message on a live map of the team, the shared Brain keeps what the team knows, and actions that change something wait for your approval.
