What I Learned Building Agents for Multi-Agent Systems

What Are Multi-Agent Systems?

Imagine a rescue team coordinating efforts to save lives during a natural disaster: each member has a defined role—some locate survivors, others provide medical aid, while logistics manage resources. Multi-agent systems operate similarly. These systems consist of multiple specialized AI agents working together to solve complex problems, each with a clear role in achieving a shared goal.

Ideal Use Cases for Multi-Agent Systems

High-quality, multi-step solutions requiring precision and collaboration.
Tasks with interdependent workflows, where one agent's output serves as another's input.
Industries like healthcare, logistics, and finance, where precision and scalability are paramount.

However, due to their complexity, these systems are resource-intensive. Use them only where their capabilities provide significant advantages over single-agent systems.

Key Lessons Learned

1. Simplicity and Single Responsibility

Agents perform best when they handle one task well. Overloading them with multiple responsibilities increases errors and complicates debugging.

Real-World Application

Good Design: An agent tasked solely with summarizing text into bullet points.
Poor Design: An agent required to analyze, transform, and visualize data within one workflow.

This mirrors the software development principle of single responsibility, where modularity leads to cleaner, more maintainable systems. This also lends itself well to using cheaper smaller models, which are easier to manage and scale.

Quick Tips

Break workflows into discrete, manageable steps.
Regularly review agents' roles for scope creep.

2. Use Natural Language Inputs and Outputs

Natural language is the backbone of multi-agent systems. Unlike traditional rigid APIs, these systems thrive on structured but flexible communication. For that reason I would recommend using natural language for both inputs and outputs rather than formats like JSON which is commonly used in APIs.

The Power of Flexibility

Rigid structures can stifle creativity and hinder efficiency. Multi-agent systems excel when you balance structured tasks with room for dynamic adaptation. For example, rather than predefining exact output formats, design prompts that guide agents with clear but flexible instructions.

Best Practices

Provide rich context for tasks, ensuring agents understand their goals.
Avoid micromanaging responses—trust the system to adapt.
Craft prompts with examples to define both structure and room for creativity which leads into the next step.

3. Provide Examples to Guide Agents

Examples are crucial for aligning agent outputs with expectations, especially in workflows requiring nuance and precision.

How Examples Make a Difference

Clear examples act as a blueprint, reducing ambiguity and ensuring consistent results. For instance, if an agent must categorize text, a sample input and expected output create a shared understanding.

Step-by-Step Implementation

Present a sample input (e.g., a paragraph of text).
Offer a sample output (e.g., categories with descriptions).
Refine prompts with feedback based on performance.

Practical Example

Task: An agent categorizes support tickets.
Example Input: "The app crashes when I open it."
Example Output: "Category: Bug Report | Priority: High."

Conclusion

Building multi-agent systems is like assembling a high-performing team: success depends on clear roles, effective communication, and thoughtful guidance. By prioritizing simplicity, leveraging natural language, and providing examples, developers can unlock the full potential of these systems.

Whether you’re optimizing supply chains, creating dynamic customer service workflows, or tackling complex problems in healthcare, multi-agent systems offer a versatile, powerful approach to AI solutions. As these systems evolve, their ability to adapt, collaborate, and innovate will redefine what's possible in AI.