msitarzewski/agency-agents: 230+ AI Agents for Your Virtual AI Team

msitarzewski/agency-agents: 230+ AI Agents for Your Virtual AI Team

Artificial intelligence is moving beyond the idea of using one chatbot for everything. Today, developers and businesses are increasingly experimenting with specialised AI agents that can perform specific roles such as software development, design, research, marketing, project management and security.

One GitHub project that has attracted significant attention in this area is msitarzewski/agency-agents, popularly known as The Agency.

The project is designed as a collection of specialised AI agents that behave more like virtual members of a professional team. Instead of giving a generic AI assistant a new role every time you start a task, you can select an agent designed specifically for that type of work.

With 230+ specialised agents, support for multiple AI coding environments, installation tools and a dedicated desktop application, the project offers an interesting approach to building an AI-powered virtual workforce.

What Is msitarzewski/agency-agents?

msitarzewski/agency-agents is an open-source collection of specialised AI agent definitions available on GitHub.

The basic idea is simple. Instead of treating AI as one general-purpose assistant, The Agency divides different responsibilities among specialised agents.

For example, you might use one agent for frontend development, another for backend engineering, another for security, another for product planning and another for marketing.

Each agent is designed around a particular role and includes information about its identity, mission, workflow, communication style and expected deliverables.

This makes the project more than just a collection of random AI prompts.

Why Is Agency Agents Getting Attention?

The biggest attraction of Agency Agents is specialisation.

A general AI model can perform many different tasks. However, when you repeatedly work on a large project, it can be useful to have predefined roles for different responsibilities.

Think of it like building a virtual company.

Instead of asking one AI:

"Do everything for me."

you can approach your project as if you have a team:

• A developer for coding

• A designer for user experience

• A researcher for information gathering

• A security specialist for security reviews

• A marketing specialist for promotion

• A project manager for planning

• A technical writer for documentation

The underlying AI model may still be doing the work, but the agent definition provides a more structured role and workflow.

More Than Just AI Prompts

One of the interesting aspects of Agency Agents is that its files are structured around specific roles rather than being simple one-line prompts.

An agent can define its personality, responsibilities, workflow, communication approach and expected outputs.

That means the user can reuse the same specialist across different projects instead of recreating a similar prompt every time.

For people who regularly use AI for development or business tasks, this can make workflows more organised.

230+ Specialised AI Agents

The project has grown into a large collection containing more than 230 specialised agents.

The available agents cover a wide range of areas, including:

• Software engineering

• Frontend development

• Backend development

• DevOps

• Security

• Product development

• Design

• Marketing

• Research

• Finance

• Project management

• Content and communication

• Game development

• Other specialised professional roles

This large selection is one of the reasons the project stands out from a simple collection of AI prompts.

How Does The Agency Work?

The concept behind Agency Agents is relatively straightforward.

First, you identify the type of work that needs to be completed. Then you select an appropriate specialised agent.

The agent provides a predefined role and workflow that can guide the AI through the task.

For example, if you are developing a website, you might use a frontend development agent for the interface, a backend agent for APIs and a security-focused agent to review potential vulnerabilities.

The idea is to create a team of AI specialists rather than relying on a single generic personality.

Domain-Focused Expertise

Each agent is built around a particular professional role.

This is useful because different jobs require different priorities.

A frontend developer might focus on:

• User interfaces

• React components

• Accessibility

• Responsive layouts

• Performance

A security-focused agent, on the other hand, may concentrate on:

• Vulnerabilities

• Authentication

• Permissions

• Secure configuration

• Risk assessment

The role itself provides additional context before the actual task begins.

Personality-Driven Agents

Agency Agents also puts emphasis on personality and communication style.

This means agents are not defined only by what they should accomplish. Their interaction style and approach to problems can also be part of the definition.

This may make conversations feel more consistent.

For example, a technical reviewer may be direct and critical, while a creative specialist may be more experimental and idea-oriented.

Deliverable-Focused Workflows

Another important feature is the focus on deliverables.

AI output becomes much more useful when you know exactly what you expect at the end of the task.

Depending on the agent, the expected result could be:

• Code

• Documentation

• Research

• Design specifications

• Project plans

• Checklists

• Metrics

• Technical recommendations

• Marketing material

This helps move AI usage from casual conversations towards repeatable workflows.

Production-Oriented Processes

The project is designed with practical workflows and success criteria in mind.

However, users should not misunderstand this point.

An AI agent is not automatically equivalent to a human senior engineer.

Even if an agent has an excellent workflow, its output still needs to be checked, tested and validated.

For production systems, human review remains important.

What Types of AI Agents Are Available?

One of the most interesting things about The Agency is the variety of roles available.

You are not limited to software development.

The project covers many professional areas, making it possible to assemble different types of virtual teams depending on your requirements.

Engineering Agents

Engineering is one of the most obvious use cases.

Developers can use specialised agents for different aspects of software development.

For example, an engineering team could potentially use separate AI specialists for frontend development, backend development, DevOps, security, debugging and technical architecture.

This can be particularly useful for developers working on large applications.

Design and Product Agents

Building a successful application is not only about writing code.

You also need to think about:

• User experience

• Product requirements

• Interface design

• User journeys

• Feature planning

• Usability

Design and product-focused agents can help developers and startups approach these areas separately.

This is especially useful for small teams where one person may be responsible for product, development and design.

Marketing and Social Media Agents

The Agency also extends beyond traditional technical work.

Marketing-related agents can help with activities such as content planning, campaign ideas, positioning and community-related work.

For an independent developer or startup founder, this could be useful because building a product is only half the challenge.

You also need people to discover it.

Research, Finance and Project Management Agents

The project also includes agents designed for research, finance and project-management-related tasks.

This opens up another possibility.

Instead of using AI only for coding, you can potentially use it throughout the complete project lifecycle.

For example:

Research → Product Planning → Design → Development → Testing → Marketing → Documentation

Different specialised agents can potentially participate at different stages.

Agency Agents Desktop App

Another interesting development is the Agency Agents desktop application.

Instead of relying completely on command-line installation, users can use the desktop application to browse available agents and manage installations.

The application is designed for Windows, macOS and Linux.

This makes the project more accessible to users who are not comfortable working entirely from the command line.

The desktop application is particularly useful if you want to browse a large number of agents and select only the ones relevant to your workflow.

How to Install Agency Agents

There are several ways to install and use the agents.

The simplest option for many users is the desktop application.

Developers who prefer the command line can use the installation scripts provided by the project.

There is also a manual approach where individual agent files can be copied into the appropriate directory for the AI development environment.

Installing Agents for Claude Code

Claude Code users can install Agency Agents using the project's installation scripts.

For example, the repository provides commands for installing agents into Claude Code.

A typical workflow can look like:

./scripts/install.sh --tool claude-code

Users can also select particular divisions rather than installing every available agent.

This is useful because installing hundreds of agents may not be necessary for every project.

If you are building a web application, for example, you may only need engineering, design and security-related agents.

Using Agency Agents With Other AI Tools

One of the strongest features of the project is its support for multiple AI coding environments.

Depending on the current supported integrations, users can work with tools such as:

• Claude Code

• Cursor

• Codex

• Gemini CLI

• OpenCode

• Copilot

• Aider

• Other supported AI development tools

This makes the project more flexible than a system designed exclusively for one AI assistant.

The important idea is that the agent definition can be reused across different AI development environments.

What Are Agency Agents Runbooks?

Runbooks take the idea of specialised agents one step further.

Imagine that you are starting a new software project.

Instead of manually deciding which AI agent should handle each part of the project, a Runbook can help define a suitable team for a particular scenario.

For example, a project could require:

1. Product planning

2. UI design

3. Frontend development

4. Backend development

5. Security review

6. Testing

7. Documentation

Rather than selecting every specialist individually, a predefined team configuration can make the process more repeatable.

This is especially useful for people who regularly start similar types of projects.

Who Should Use Agency Agents?

Agency Agents can be useful for several types of users.

Developers

Developers can use specialised agents for coding, debugging, architecture, documentation and security-related work.

Startup Founders

A startup founder often has to handle product development, marketing, research and planning simultaneously.

A collection of specialised AI agents could provide assistance across these different areas.

Small Businesses

Small businesses may not have dedicated specialists for every function.

AI agents can potentially help with research, content, marketing, documentation and planning.

Content Creators

Content creators can explore research, writing, marketing and social-media-focused agents.

However, human editing remains important if the content needs to demonstrate expertise and originality.

Project Managers

Project managers can use AI specialists to assist with planning, documentation, task breakdown and project coordination.

Advantages of Agency Agents

There are several potential advantages to using this approach.

Specialisation

Instead of one generic AI assistant, you get predefined specialists for different jobs.

Reusable Workflows

Once you find an agent that works well for a particular task, you can reuse it across projects.

Large Selection

With more than 230 agents, there is a wide range of roles to explore.

Open Source

The project is open source and released under the MIT licence, making it accessible for personal and commercial use subject to the licence terms.

Multiple AI Tools

The ability to work with multiple AI coding environments makes the system more flexible.

Desktop Application

The desktop application provides a simpler way to browse and install agents.

Limitations and Things to Consider

Agency Agents is interesting, but it is not magic.

The first limitation is that the quality of the final result still depends heavily on the underlying AI model.

A well-designed agent cannot completely compensate for an unsuitable model or insufficient project context.

The second limitation is that having hundreds of agents does not necessarily mean you should use hundreds of agents.

In fact, installing too many agents can make a workflow unnecessarily complicated.

A better approach is to start with a small team.

For example:

Frontend + Backend + Security + Product

Then add other specialists only when they are actually needed.

Security Considerations

Security is particularly important when working with AI coding tools.

You should understand what scripts you are executing and what permissions your AI tools have.

Never place sensitive API keys, passwords, private tokens or production credentials inside agent files.

You should also avoid giving an AI tool unnecessary access to sensitive folders or production systems.

Before executing installation scripts from any open-source project, it is good practice to review what the scripts actually do.

AI can accelerate development, but convenience should never replace security.

Is Agency Agents Free?

The main Agency Agents repository is released under the MIT License.

This allows broad use of the project, including commercial use, subject to the conditions of the licence.

The open-source nature of the project is one of its biggest attractions.

Developers can inspect the agent definitions, customise them and potentially create their own specialised agents.

Why Is Agency Agents Important?

The most interesting part of this project is not simply the number of agents.

It represents a broader change in the way people may use AI.

The old model was:

One AI → One conversation → One task

The emerging model is:

AI model → Specialised agents → Team → Workflow → Deliverables

That is a significant shift.

Instead of thinking about AI as a single assistant, developers can start thinking about AI as a collection of specialised digital workers.

Of course, these agents are still powered by AI models and require human supervision.

But the organisational structure can make AI much more useful for repeatable professional work.

Final Verdict

msitarzewski/agency-agents is definitely worth exploring if you are interested in AI-powered development and productivity.

The project's biggest strength is its focus on specialisation.

With more than 230 agents covering areas such as engineering, design, marketing, research and project management, it provides a large library from which users can build their own virtual AI teams.

The desktop application makes installation easier, while Runbooks provide a way to organise groups of agents around particular project scenarios.

The most important thing, however, is to use the agents intelligently.

You do not need 230 AI agents.

Start with the few specialists that match your actual work, test their output, refine your workflow and expand the team only when necessary.

For developers already using tools such as Claude Code, Cursor, Codex or Gemini CLI, Agency Agents could become an interesting addition to the AI development toolbox.

Frequently Asked Questions

1. What is msitarzewski/agency-agents?

msitarzewski/agency-agents is an open-source collection of specialised AI agents designed for different professional roles, including software development, design, marketing, research and project management.

2. How many agents are available in Agency Agents?

The project currently contains 230+ specialised AI agents, covering a wide range of professional roles and workflows.

3. Can Agency Agents be used with Claude Code?

Yes. The project provides installation options for Claude Code along with support for several other AI coding environments.

4. Is Agency Agents free to use?

The main Agency Agents repository is open source and released under the MIT License. Users should review the licence terms for their particular use case.

5. Does Agency Agents have a desktop application?

Yes. Agency Agents also has a desktop application designed to make browsing and installing agents easier across supported AI coding tools.

Conclusion

The rise of projects such as msitarzewski/agency-agents shows how quickly AI development workflows are changing.

Instead of relying on a single generic AI assistant, users can create a virtual team of specialised AI agents, each designed for a particular responsibility.

Whether you are a developer, startup founder, content creator or business owner, the concept is worth watching.

The future of AI may not simply be about having a smarter chatbot.

It may be about building the right AI team for the job.