Lesson Overview

Choosing between a single agent and a multi-agent system often shapes the scale and maintainability of an AI project. This lesson clarifies where to start and what to consider as your needs grow. In the world of AI agents, a single agent system handles all tasks alone, making it a straightforward solution for many problems, especially when just starting out. As projects expand, splitting responsibilities among several agents—each with its own specialty—can lead to better organization and efficiency. For example, an agent focused solely on research can support a sales agent, while another handles customer support, mirroring how human teams work inside organizations.

While it’s tempting to jump into complex, multi-agent systems, especially after hearing about advanced uses in robotics or self-driving technology, the simplest solution is almost always the best starting point. When in doubt, begin with one agent. Only add more agents when the task becomes too big or specialized. Sometimes, an automated script or basic automation might even be better than using an AI agent at all.

Who This Is For

Whether you’re exploring AI agents for the first time or refining your workflow, this lesson is for you if you:

• Are new to building AI agent systems
• Need to structure your AI projects efficiently
• Work in business, education, or support roles considering task automation
• Want to learn how to scale from simple solutions to more complex systems
• Manage or work alongside teams looking to automate routine work

Where This Fits in a Workflow

Deciding between a single agent and multi-agent system usually happens at the beginning of planning an AI project. If your current workflow involves only a few straightforward tasks, setting up a single agent keeps things simple and manageable. For instance, a single agent can handle submitting research summaries on a topic. As demands increase—such as the need to split tasks between research, sales prospecting, and support inquiries—introducing specialized agents helps keep each process focused and efficient. This lesson provides a decision-making foundation that supports both small pilots and scalable, more complex deployments.

Technical & Workflow Benefits

Traditionally, assigning all tasks to a single automation or even one person can result in bottlenecks and confusion. A single agent system, when chosen carefully, offers clarity and speed for projects that aren’t overly complex. As your needs change, using multiple agents—each focused on a specific area—lets you organize your system just like a well-run team. For example, handing off sales leads from a research agent to a sales agent mimics real-world departments and keeps workflows consistent. This structure supports better oversight, makes maintenance easier, and can scale as your operations grow, without over-complicating early stages.

Practice Exercise

Think of a project you want to automate, such as handling customer support emails or compiling research for a report.

1. List all the tasks required to complete this project.
2. Decide which tasks could be handled by a single agent, and where it might make sense to introduce a separate agent for a specialized role.

Reflect: If you used only one agent, would it be simpler, or would breaking tasks into focused agents offer clarity as the project grows? Consider how this decision would impact the workflow.

Course Context Recap

This lesson builds on your introduction to AI agents by providing guidance on structuring your system intelligently. You’ve moved from understanding what agents are to deciding how many are appropriate for your workflow. Next, you’ll dive deeper into setting up and configuring your chosen agent setup. Continue through the course to explore more practical applications and build confidence in designing the right AI workflow for your needs.