My Opinion: Here's Why Multi-Agent Systems Are Better Than a One-Size-Fits-All Agent

In a recent blog post, I shared a bit about my AI research assistant and how I’m scaling its intelligence. Part of my ongoing work has been to build a team of collaborative AI researchers—each specializing in different tasks. This approach has led some people to ask: Why not just have one AI agent that does everything?

While it’s technically possible to create a single, all-encompassing AI agent, I choose not to for several key reasons. Let’s break down why multi-agent systems are not only more efficient but also more adaptable—especially when collaboration and modularity—breaking tasks into independent parts—come into play.

Collaboration: Building Bridges, Not Walls

One of the most important reasons for using multiple agents is to facilitate collaboration. Let’s take my research assistant as an example:

• The assistant’s primary role is to fetch documents, analyze them, and gather feedback from me, which it then stores in a spreadsheet or database.

• Instead of making the same assistant also responsible for analyzing patterns in that feedback, I hand off the data to a second agent specifically designed for data analysis.

Why This Matters:

Imagine I’m working with a team of data analysts who specialize in finding trends and insights. Since my pattern detection and data analysis agents are separate, I can easily integrate their expertise without disrupting the core function of my primary research assistant.

Here are the tasks of the early AI research team members I’m building:

• The retrieval agent does what it’s good at: pulling data and collecting feedback.

• The data analysis agent does what it’s good at: processing data to find insights and patterns.

• The pattern detection agent then takes those insights and identifies broader trends.

• This modular setup makes it easy to collaborate and leverage different skill sets without creating a tangled mess of tasks within one agent.

Example:

Think of it like a newsroom: You wouldn’t expect one reporter to write, edit, publish, and analyze reader engagement all by themselves. Instead, you have:

• Writers who create content.

• Editors who ensure quality.

• Data analysts who study readership trends.

• Social media managers who engage with the audience.
  Each role is specialized, and when they work together, the result is a well-functioning newsroom.

Modularity and Adaptability: One Task, One Agent

Another reason for separating tasks into multiple agents is modularity. When each agent has a clearly defined role, it becomes easier to:

• Update or modify one part without affecting the whole system.

• Troubleshoot problems since you know which agent is responsible for what.

• Scale the system by adding new agents as needed.

Why This Matters:

Let’s say an organization wants to use my research system. They may already have internal tools for data analysis or visualization. Instead of overloading my primary assistant with all these functions, I can:

• Plug in their existing tools to the pattern detection agent.

• Keep the document retrieval agent doing what it does best.
  This makes it easier for any-given organization to adopt and integrate the system without requiring them to overhaul their existing processes.

Performance and Efficiency: Less Is More

One major risk of having a single, massive agent is that it becomes bloated and inefficient. As you pile on more functions, the risk of errors increases. A failure in one part can affect the entire system.

• With separate agents, each can be optimized individually.

• They can run concurrently, rather than bottlenecking as one overloaded process.

Why This Matters:

For example, if the retrieval agent I created encounters an error while accessing a website or dataset, it doesn’t affect the pattern detection agent. They continue working independently, so one issue doesn’t cause a complete system failure.

While it’s tempting to think one AI agent can do it all, that’s rarely practical or efficient. By building a team of specialized agents, I can easily adapt, collaborate, and scale as needed. It’s not about making one tool that does everything poorly—it’s about creating specialized tools that work well together.