A basic model for making AI agents work together
AI agent collaboration means using several role-based AI agents instead of asking one system to do everything. A single model can handle one-off tasks such as writing an email or summarizing a report, but real business work often involves tickets, approvals, monitoring, , , , and many handoffs. An AI agent can take a goal and context, plan steps, use tools such as APIs, databases, browsers, and code execution, observe the results, and adjust its plan.
In , agents with different roles communicate so they can solve a larger task more reliably than one agent could handle alone. The basic idea is similar to a well-run group project, with separate roles for coordination, research, , and checking.
Key points
- AI agent collaboration is meant for multi-step work, not just one-off prompts.
- An AI agent can plan, use tools, check results, and change course.
- splits work into roles such as coordination, research, , and review.
- The goal is to handle complex workflows more reliably than one agent working alone.
- Token savings depend on keeping agent communication focused and avoiding duplicate work.