Scaling AI Teams
Part of the Vibe Coding: Multi-Agent Engineering course
You have learned to lead a team of AI agents: giving each a focused role, handing work between them with context, running a separate review, and managing the project through sprints and priorities. All of that works beautifully on a project small enough for one agent to hold in its head.
Early on, one agent could see your whole product at once: every file, every decision, the full picture. That is what made it feel fast. A large project is simply one that has grown past that point, where no single agent can hold all of it in its context (the limited window of information an AI can consider at one time). ...
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Start this lessonScaling AI TeamsWhat you'll learn
- A large project outgrows a single agent's context and must be split into parts with clear boundaries
- A boundary defines what a part owns and the one well-defined way other parts interact with it
- Team coordination keeps many agents on the same goal without conflicting or duplicating work
- A shared source of truth holds docs and decisions outside any single conversation so context does not vanish
- Long-term development means onboarding new agents, guarding against drift, and keeping the plan current
- Coordination and shared knowledge, not raw building speed, are the real limits at scale
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All lessons in this course
12 lessons- 1Thinking Like an Engineering Leader20 min
- 2Creating Specialized AI Agents20 min
- 3Giving Every Agent a Role20 min
- 4Managing AI Collaboration30 min
- 5AI Code Reviews30 min
- 6AI Project Management20 min
- 7AI DevOps30 min
- 8AI Security Teams30 min
- 9AI Operations30 min
- 10Building Your Own AI Workflow20 min
- 11Scaling AI Teams20 min
- 12Becoming an AI Engineering Leader45 min
