To build a useful multi-agent AI system, model the agents after your existing human team. Create specialized agents for distinct roles like 'approvals,' 'document drafting,' or 'administration' to replicate and automate a proven workflow, rather than designing a monolithic, abstract AI.

For processes that span multiple departments, like customer onboarding, a 'bridge agent' can maintain the complete picture. It prevents context loss and broken handoffs that occur when each team's individual agent operates in a silo, solving for work that 'sits between' teams.

To manage a team of specialist agents, designate one as a 'Chief of Staff' or manager. This manager agent can conduct bi-weekly performance reviews of the other agents, grade their output, and send a summary report to the human user, elevating your role from micromanaging tasks to high-level strategic oversight.

For large engineering tasks, create a hierarchy of AI agents. A "Chief of Staff" bot delegates to an "Eng Lead," which breaks down work and supervises individual "Engineer" bots. This structure enables massive task parallelization and orchestration.

AI expert Allie Miller runs her life with 34 AI agents organized under a chief of staff agent. This "workforce" model with specialized roles and even a "note-taker" agent is a more systemic and powerful approach than automating isolated tasks, requiring a shift in thinking from tasks to systems.

Avoid building one AI agent to do everything. Instead, create a hierarchy with a 'manager' agent that delegates tasks to specialized sub-agents (e.g., for coding, research). This prevents context overload and performance degradation, mirroring an effective human team structure for scalable automation.

Separating AI agents into distinct roles (e.g., a technical expert and a customer-facing communicator) mirrors real-world team specializations. This allows for tailored configurations, like different 'temperature' settings for creativity versus accuracy, improving overall performance and preventing role confusion.

Instead of using simple, context-unaware cron jobs to keep agents active, designate one agent as a manager. This "chief of staff" agent, possessing full context of your priorities, can intelligently ping and direct other specialized agents, creating a more conscious and coordinated team.

Instead of a monolithic AI, create a team of agents with specific roles (e.g., 'Debbie the assistant,' 'Soren the engineer'). This human-like model makes it easier to manage capabilities, control access, and conceptualize the system's functions because it maps to our innate understanding of human teams.