It’s 2025, and coding agents are old news. It has become commonplace for programmers to work with an AI-powered agent, such as Copilot or Cursor, to help accelerate the software development process. 

Today, innovative programmers are taking AI agents a step further. They’re building entire teams of coding agents, an approach that doubles down on the efficiency and scalability of AI-assisted coding tools. 

That said, replacing a single coding agent with a multi-agent team also presents new challenges, not least in the realm of deciding exactly how to construct and orchestrate agents. 

Read on for details as I unpack the reasons why developers are pivoting toward the coding agent team model, as well as the top coding agent frameworks to consider. This article draws on my personal experience leveraging teams of coding agents to streamline my own software projects over the past several months. 

What is a Coding Agent Team? 

A coding agent team is exactly what it sounds like: A group of AI agents that assist with coding. 

Agent teams are innovative because until recently, most developers relied on just a single AI agent to help them write, build, test, and/or deploy code. They used AI-assisted development tools that were more or less designed to operate as if they were a single, full-stack developer and DevOps engineer, capable of handling all aspects of the software development process and life cycle. 

Coding agent teams replace this approach with a collection of agents, each tailored for a different development task. The exact number of agents and their roles can vary, but as an example, one agent might write frontend code, while another develops an application backend, a third writes and executes tests and a fourth reviews test results to determine whether code is fit for deployment. And alongside all of these is a “team lead” agent, responsible for receiving high-level instructions from a human developer and delegating them to individual agents on the team. 

The Benefits of Multiple AI Agents for Coding 

Switching toward a multi-agent approach to AI-assisted software development offers a range of benefits that help to make the development process even faster and smoother than it would be with a traditional, single-agent model. 

1. Faster development 

The ability to have multiple agents working concurrently increases development velocity. When you can write your frontend and backend code in parallel, while also beginning the testing and review process as soon as code starts to appear, you go from idea to release much faster than you would if you had a single agent performing these tasks sequentially. 

This benefit is especially important in the context of large-scale projects, where the ability to work on multiple application components in parallel can significantly speed up the development process. 

2. Agent specialization 

Agents tend to work more efficiently and effectively when they are optimized for specific tasks. For example, an agent designed for frontend development is likely to produce higher-quality frontend code by connecting to a model optimized for this purpose. 

In this sense, a coding agent team is preferable to relying on a generic agent to write all of your code. The latter may be able to do it all, but not in an optimal way. 

3. Simulating Human Teams 

The multi-agent model makes it easier to align AI-assisted development workflows with the structure of human software development teams. Each AI agent can handle tasks that would fall to different programmers, such as (again), frontend and backend development. 

In this way, agent teams allow human participants in the development workflow to work alongside agents that are customized for their particular domains, further boosting productivity and efficiency. 

4. Turning Developers Into Product Owners 

Even more than single coding agents, agent teams empower developers to act as product owners who can oversee all aspects of the development process. Developers define what they want to happen, then hand implementation over to agents. 

The result is less time manually coding and more time focusing on application features and optimizations – leading ultimately to a better product delivered in less time. 

Practical Approaches to Building AI Agent Teams in Software Development 

Putting the multi-agent model into practice can be a bit challenging because, like many other agentic AI technologies, solutions for building and managing teams of AI agents remain fractured. Various frameworks exist, which with different strengths and limitations. Thus, a challenge for developers is selecting the coding agent framework that best aligns with their overall goals and priorities. 

Having experimented with multiple AI agent frameworks for software development, I’ve landed on the following as the leading options at present (although it’s important to note that the fast-evolving nature of this space means that this list could look different just months from now): 

• MetaGPT: Simulates project management, development and quality assurance roles. A good option for end-to-end codebase creation. 

• CrewAI: Uses a role-based, modular agent approach. Excels for structured team workflows that are broken into distinct development tasks. 

• AutoGen: An event-driven agent framework from Microsoft. Ideal for highly automated agentic software development workflows. Offers the advantage of multi-model support (including Claude and OpenAI/GPT). 

• OpenCode: A lightweight, CLI-based framework for managing agent interactions. Ideal for developers who prefer terminal-centric coding workflows. 

• Claudeflow: An open source agentic AI framework that integrates natively with Claude. Uses a team lead model, with a lead agent responsible for delegating tasks to other agents. Good for workflows with clearly defined development roles. 

Conclusion: Taking AI-Assisted Coding to the Next Level With Agent Teams 

On balance, I should note that not every development project or need is best served by a coding agent team. For very small codebases, a single agent is usually better. It may also be appropriate to use just one agent to address discrete tasks, like remediating a specific bug. 

But for most software development projects, pivoting toward a team of coding agents is a faster, more productive way to work. The biggest hurdle to clear is simply deciding which agent framework to use. From there, programmers can deploy a fleet of agents tailored for their unique software development needs.