AI agents are designed to handle complex tasks that a standalone large language model (LLM) cannot complete using its existing knowledge alone. To solve these problems, agents often need to work together, with each agent responsible for a specific role.
This approach is similar to building a human team. Some people do the work, others plan, research, review, or coordinate. When these roles work together, the team can solve more complex problems and deliver better results.
So, how do you build an effective team of AI agents?
1. Understand the Key Roles of an AI Agent Team
Imagine building an AI agent that develops a mobile application. It needs to understand the user’s requirements, plan the architecture, write the code, test the application, and prepare it for release.
Instead of asking one agent to do everything, you can divide the work among specialized agents.
The Doer: Gets the Work Done
The doer is responsible for completing specific tasks, such as writing code, creating content, or implementing a feature.
Like a junior developer on a human team, the doer can handle individual tasks but may struggle to manage an entire project without guidance.
The Planner: Breaks Down Complex Problems
The planner takes the user’s request and turns it into a clear, actionable plan.
For a mobile app, it might first identify the user requirements and then design the application architecture before development begins.
The planner’s job is to break a complex problem into smaller tasks, identify the skills needed for each one, and document the steps required to complete the project.
The Tool Operator: Connects AI to External Tools
Some tasks require more than generating text. An agent may need to call APIs, run Python code, access databases, or interact with web services.
The tool operator handles these interactions by preparing the required inputs, executing the appropriate tool calls, and returning the results.
This allows an AI agent to perform actions and access information beyond what it already knows.
The Learner: Gathers Information
The learner gathers information from external sources to help the team make better decisions.
For example, when developing a mobile app, it might research competitors, study user feedback, or explore industry trends to identify useful features.
It then filters the information and shares relevant findings with the planner or doer.
This role can use techniques such as Retrieval-Augmented Generation (RAG), which allows an AI system to retrieve relevant information from external data sources before generating a response. It can also rely on more traditional search and data-retrieval methods.
The Critic: Reviews the Work
The critic checks whether the work meets the required standards.
It might review generated code, identify errors, check for unsupported claims, or run quality assurance tests.
A critic can also compare several possible solutions and select the strongest one.
This feedback helps the team identify problems early and improve the final result.
The Supervisor: Keeps Everything on Track
The supervisor monitors progress and helps ensure that tasks are completed correctly.
It can work at different levels. A task-level supervisor checks an individual agent’s work, while a project-level supervisor coordinates the overall process.
If an agent gets stuck, produces an error, or fails to complete a task, the supervisor can identify the problem and help determine the next step.
The Presenter: Communicates the Final Result
Once the work is complete, the presenter brings everything together and explains the result to the user.
For the mobile app example, it might summarize the requirements, explain the architecture, describe the implemented features, and report the results of testing.
Its purpose is to turn the team’s work into a clear, useful final response.
Not every AI project needs all these roles. The right combination depends on the complexity of the task.
2. How These Roles Work Together
Some popular AI agent patterns combine several of these responsibilities.
One example is ReAct, which stands for Reasoning and Acting. This approach combines three main steps:
- Reason: Decide what needs to happen next.
- Act: Use a tool or perform an action.
- Observe: Review the result and decide what to do next.
The process continues until the task is complete, and the system then communicates its answer.
ReAct is a useful starting point for building agents that need to reason, use tools, and respond to the results of their actions.
As tasks become more complex, the system may need additional roles, more detailed planning, specialized execution, and stronger feedback loops.
3. How to Make Each Agent Better at Its Job
Creating different roles is only the first step. Each agent also needs the right instructions, model, training, and information to perform effectively.
Prompting: Give Clear Instructions
Every agent needs instructions that explain its responsibilities, goals, limitations, and expected output.
For example, a coding agent might be instructed to follow existing project conventions, run relevant tests, and retry a failed operation when appropriate.
Clear instructions help agents focus on their assigned tasks and produce more consistent results.
Model Selection: Choose the Right Model
Different models have different strengths. Some are better at reasoning, others at coding, and others at fast, straightforward tasks.
Choose a model based on the role it needs to perform, considering its capabilities, speed, cost, and reasoning performance.
A complex planning task may benefit from a strong reasoning model, while a simple, repetitive task may not require the same level of capability.
Model Tuning: Train for Better Results
Model tuning can help a model perform more reliably on a specific task.
One approach is fine-tuning, which uses examples of desired behavior to adjust a model’s parameters. Good-quality examples are essential, and preparing the training data and running the tuning process can require significant time and computing resources.
Because of this, fine-tuning is not always necessary. Clear prompts, suitable models, and relevant context may be enough for many tasks.
Context: Provide the Right Information
Agents need access to the information required to do their jobs.
This might include project documentation, source code, databases, business rules, or previous task results.
However, providing more information is not always better. Too much irrelevant context can distract an agent or make it harder to identify what matters.
The goal is to give each agent the information it needs without overwhelming it.
4. Start Small and Grow as Needed
You don’t need a large team of AI agents from the beginning.
Start with a few essential roles that can solve the problem at hand. As the system becomes more complex, identify weaknesses and introduce additional roles where they provide real value.
For example, a basic coding agent might initially need only a planner and a doer. Later, you might add a researcher to gather requirements, a critic to test the code, and a supervisor to coordinate the entire development process.
The key is to build your agent team around the work that needs to be done, rather than adding roles simply for the sake of complexity.
The main lesson is simple: Effective AI agent systems rely on more than powerful models. They combine clearly defined roles, suitable tools, relevant information, and continuous feedback. When these elements work together, AI agents can handle more complex tasks and deliver more reliable results.
