How to Build a Team of AI Agents: Roles, Feedback, and Teamwork Explained

Most real-world problems are too complex for a single AI model call. Just like human teams, powerful AI systems work best when you break work into roles—planners, doers, critics, supervisors, and communicators—that collaborate toward one goal.

In this guide, we’ll walk through how to design a “team” of AI agents inside your system, what roles you might need, and how to make each one good at its job.

Why Complex AI Systems Need Teams of Agents

A single large language model (LLM) is great at answering questions or drafting content, but it struggles with long, multi-step projects on its own—like building a full mobile app, running an investigation, or managing a workflow end-to-end.

To handle these kinds of tasks, you can structure your system as a team of specialized agents. Each agent focuses on one part of the work and passes results to the next, just like people on a project team. Over time, this structure gives you:

• Better reliability (because each step is checked and constrained)
• More control (you can see where things go wrong and fix that piece)
• Higher quality outputs (through planning and internal feedback loops)

If you’re new to this way of thinking, it pairs well with understanding the basics in Don't Build AI Agents Without Understanding These 5 Core Concepts.

The Core Roles in an AI Agent Team

Imagine you want an AI system to design, build, test, and describe a mobile app. From the user’s perspective, it’s one request. Under the hood, it’s a team effort.

Here are the main roles (or subagents) you’ll typically want to think about.

1. The Doer: Executes the Work

The doer is your junior teammate. It doesn’t own the big picture; it just carries out specific tasks.

Examples of what a doer agent might handle:

• Writing a specific function or file in the app’s codebase
• Drafting a single section of documentation
• Generating test cases given a clear spec

On its own, a doer can’t reliably build an entire app. But when it’s given small, well-defined tasks by other roles, it’s very effective.

2. The Planner: Breaks Down the Problem

The planner is the strategist. It takes the user’s request and turns it into a structured plan of steps.

In the mobile app example, you might have two planning phases:

• Requirements planning: Extracting and organizing user requirements (features, user flows, constraints).
• Architecture planning: Designing the app’s structure, components, and data flows before any code is written.

The planner’s core skills are:

• Decomposing a complex problem into smaller tasks
• Identifying what skills or tools are needed for each step
• Producing a clear, documented plan—but not doing the work itself

3. The Tool Operator: Talks to APIs and Services

The tool operator is responsible for interacting with external tools and services. It doesn’t “think” about the whole project; it focuses on calling tools correctly and returning results.

Typical responsibilities:

• Constructing structured tool calls (e.g., API requests, Python functions, web services)
• Filling in required arguments from the current context
• Returning tool outputs in a clean, usable format for other agents

In many systems, this is where you wire your LLM into your existing stack—databases, code execution, third-party APIs, and more.

4. The Learner: Brings in Outside Knowledge

The learner keeps your agent team informed about the outside world. It’s the one that “goes out” to gather information and bring it back.

In the app example, the learner might:

• Research competing apps to see what features they offer
• Pull user feedback from blogs, forums, or social media
• Collect domain-specific guidelines or best practices

Its key skills are:

• Retrieving information from external sources (search, RAG, databases, APIs)
• Filtering what’s relevant
• Feeding that information back into planning or doing steps

Often, this role is implemented as a Retrieval-Augmented Generation (RAG) flow, but it could also be a more rules-based retrieval system.

5. The Critic: Provides Feedback and Quality Control

The critic is your internal reviewer. Its job is to say, “Is this good enough?” and “What’s wrong here?”

Depending on your use case, a critic might:

• Check responses for hallucinations or factual errors
• Review generated code and write QA tests
• Compare multiple candidate outputs and pick the best one

This role can also introduce healthy “competition” among agents by scoring or ranking outputs before they move forward.

6. The Supervisor: Oversees Progress

The supervisor watches the whole process and makes sure nothing gets stuck.

You can design supervisors at different levels:

• Task-level supervisors: Embedded in specific roles to monitor progress and retry or adjust when something fails.
• Project-level supervisors: Track the overall workflow, detect where steps are failing, and decide how to recover.

Think of this as your project manager: it doesn’t do the work itself, but it keeps everything moving and helps debug failures.

7. The Presenter: Communicates Back to the User

At the end of the workflow, you need one role to assemble everything into a clear response for the user. That’s the presenter.

In the app example, the presenter might:

• Summarize the user requirements that were derived
• Explain the generated codebase and how it’s structured
• Describe what the final app does and how to use it

The presenter doesn’t change the work itself; it packages it in a way that’s understandable and useful.

Popular Role Combinations: The ReAct Pattern

Some combinations of these roles have become standard patterns. One well-known example is the ReAct pattern, which typically includes:

• Reason (planner role): Think through the problem and decide what to do next.
• Act (tool operator role): Call tools or APIs based on that reasoning.
• Observe (critic role): Look at the tool results and decide what they mean.

These steps repeat until the system is ready to produce an answer, which is then handled by a presenter-like role.

Patterns like this are great starting points. As your use cases get more complex and you need more consistency across many tasks, you’ll usually expand the team with more explicit planning, focused execution, and richer internal feedback.

How to Make Each Agent Role Good at Its Job

Defining roles is only half the work. You also need to make each subagent actually perform well. There are four main levers you can use.

1. Prompting: Clear Instructions and Behaviors

Prompts are your instructions to each role. Just like onboarding a human teammate, you need to spell out:

• What the role is responsible for
• What a good output looks like
• What to do when things go wrong (e.g., “If you get stuck, try a different approach or ask for more context.”)

Even simple behavioral instructions—like “think step by step,” “don’t assume missing data,” or “always validate inputs before calling tools”—can dramatically improve reliability.

2. Model Selection: Pick the Right Model for the Role

Not every role needs the same kind of model. Choosing the right one is like hiring the right person for a job.

Factors to consider:

• Specialization: Some models are better at coding, others at reasoning, others at fast, lightweight tasks.
• Size and cost: A small, cheap model might be perfect for simple doer tasks, while planning or supervision might deserve a larger reasoning model.
• Persona and style: For presenter roles, you may want a model that’s especially good at clear, user-friendly explanations.

Mixing models across roles can give you better performance and lower cost than using one big model for everything.

3. Model Tuning: Train on Good and Bad Examples

For high-stakes or repetitive tasks, you can go beyond prompting and tune models for specific roles.

This usually involves:

• Creating a ground truth dataset of inputs and desired outputs (and sometimes examples of bad outputs)
• Fine-tuning or retraining the model so it learns your exact definition of success

This approach is powerful but resource-intensive. It requires:

• Human effort to label and curate examples
• Compute resources to train or fine-tune the model

It’s often worth it for critical roles like code generation, QA, or domain-specific reasoning.

4. Context: Give Each Role the Right Information

Context is everything the agent can “see” when it runs—documents, database entries, previous steps, tool outputs, and more.

Designing context is similar to onboarding a new employee:

• Give them access to the systems, files, and data they need.
• Don’t overwhelm them with irrelevant information.

For example:

• The planner might need the full user prompt, past requirements, and some domain guidelines.
• The doer might only need the current task, a small slice of the plan, and relevant code files.
• The critic might need both the input and output of a step, plus any constraints or acceptance criteria.

Choosing context wisely helps agents stay focused and reduces the risk of errors or hallucinations.

Growing Your Agent Team Over Time

You don’t need all of these roles from day one. Just like a startup, you can begin with a small, scrappy team and grow as your needs evolve.

A typical progression looks like this:

• Phase 1: One or two roles (e.g., a simple ReAct-style agent) to prove the idea.
• Phase 2: Add planning and basic criticism to improve reliability.
• Phase 3: Introduce supervision, richer learning/retrieval, and more specialized doers for scale and quality.

As your system matures, you’ll fill in weaknesses, fix recurring failure points, and add specialized roles—just like hiring for a growing company.

If you’re interested in applying this thinking to content workflows specifically, it pairs nicely with the ideas in how to build a one-person AI content team with Claude skills, which uses a similar “team of roles” mindset.

The key takeaway: don’t think of your AI as a single monolithic brain. Think of it as a coordinated team of specialists. When each role is clearly defined, well-instructed, and given the right tools and context, your agents can tackle far more complex, valuable work.