The Agency: Specialized AI Specialists Ready to Transform Your Workflow

Part 1: Foundations (The Mental Model)

Most people interact with LLMs as a single, generic “assistant.” You ask a question, it gives an answer. But as developers, we know that expertise isn’t generic. A frontend developer thinks differently than a backend architect. A security engineer looks for different patterns than a rapid prototyper.

The mental model for The Agency is simple: Don’t talk to a generic AI; hire a specialized team.

Instead of spending hours crafting the “perfect prompt,” you activate a pre-configured Specialist. These aren’t just templates; they are personas with identity, mission, specific deliverables, and battle-tested workflows.

Part 2: The Investigation

When you look into the agency-agents repository, you don’t just see a list of text files. You see a highly organized structure divided into “Divisions”:

• Engineering: Frontend Developers, Backend Architects, DevOps Automators, Mobile App Builders, and more.
• Design: UI Designers, UX Researchers, Whimsy Injectors (yes, really!).
• Marketing: Reddit Community Builders, Growth Hackers, SEO Specialists.
• Testing: Evidence Collectors, Reality Checkers.
• Specialized: MCP Builders, Agentic Identity Architects.

Each specialist is defined by a markdown file that encapsulates their “soul”:

• Identity & Personality: How they speak and think.
• Core Mission: What they are trying to achieve.
• Technical Deliverables: The exact code or documents they produce.
• Workflows: The step-by-step process they follow.

Part 3: The Diagnosis

Generic prompts fail because they lack context-specific constraints. A generic AI might suggest a quick-and-dirty fix that violates your project’s architectural standards.

The Agency solves this by enforcing role-specific discipline.

Real Use-Case: The “Evidence Collector”

In the Testing Division, the Evidence Collector agent has a strict rule: “I default to finding 3-5 issues and require visual proof for everything.”

When you activate this agent, it won’t just say “LGTM.” It will actively hunt for edge cases and demand screenshots or logs to certify a feature. This is the difference between a tool and a teammate.

Technical Deep Dive: Multi-Tool Integration

The repository isn’t just a collection of markdown files. It includes a conversion and installation system that makes these agents compatible with almost every major agentic tool:

• Claude Code: Native .md support.
• Cursor: Converted to .mdc rules.
• Antigravity: Each agent becomes a “Skill”.
• Windsurf/Aider: Compiled into comprehensive convention files.

The ./scripts/convert.sh and ./scripts/install.sh tools automate the deployment of these specialists into your local dev environment.

Part 4: The Resolution

How do you actually use this? It’s as simple as bringing your team into your workspace.

Step 1: Clone and Install

git clone https://github.com/msitarzewski/agency-agents
cd agency-agents
./scripts/install.sh

Step 2: Activate a Specialist

If you’re using Claude Code or Cursor, you can now simply reference the agent.

Prompt for Frontend Developer:

“Review this React component. Use the @frontend-developer rules to ensure pixel-perfect UI and performance optimization.”

Step 3: Build a Workforce

You can chain agents together. Use the UX Researcher to define the requirements, the Backend Architect to design the API, and the Reality Checker to verify the final production build.

Final Mental Model

From: Generic AI	To: The Agency
Input: Prompt engineering	Input: Role activation
Output: Text response	Output: Professional deliverables
Context: Vague/General	Context: Domain-specific expertise
Vibe: Helpful assistant	Vibe: Specialized teammate

The Agency transforms AI from a simple calculator into a high-performance workforce. It’s not about making AI smarter; it’s about making it more disciplined, specialized, and actionable.

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Created by HoangYell. Explore the agency-agents repo today.