Say the goal. Finish it together.
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Different tasks need different team shapes. Agents can maintain their own relationships—you stay focused on whether the problem is solved.
Example · coding delivery
One common pattern—coordinate, build in parallel, review, iterate, and release. Not the only way to run a multi-agent team.
Start in chat or on the task board with what you want done.
A coordinator agent assigns the right roles to the task.
UI, logic, and data can move forward at the same time.
If it is not good enough yet, agents send it back and improve it.
Once it passes review, the output is released and ready to use.
These are common patterns—not the only ones. AI can design a team shape for your task too.
Set the role, capabilities, and models—minutes later you have a partner to work with. It can deliver docs, pages, images, and video, not just chat.
Build a partnerSet your rules once. Context carries across sessions and gets more aligned over time.
Define the role, attach knowledge and skills, then enable search, docs, MCP, and more.
Use platform models, your API keys, or CLI agents per task. The same agent can ship docs, pages, images, and video—not chat alone.
Docs, apps, images, video, and 3D—give an idea, build it together.
For people already playing with agents and trying to ship real work—not just prettier prompts.
Yes. Rules, attached knowledge, and dialog history stay on the agent. When multiple agents collaborate, context flows as needed instead of restarting from zero every task.
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