In the competition for "parameter supremacy" in large models, an open-source project that wins through "assembling experts" is rapidly sweeping through the developer community at an infrastructure-level pace.

As of March 24, 2026, the project agency-agents, initiated by developer Marek Sitarzewski, has surpassed 60,000 stars on GitHub. In just the past week, the project gained a net 23,000 stars, directly topping the global GitHub weekly growth chart, leaving many major company projects behind.

Not competing in algorithms, but in division of labor: Building a "plug-and-play" digital outsourcing team

The popularity of agency-agents is not accidental; it precisely addresses current business anxieties within companies: single general-purpose large models often "know a little about everything but excel in nothing," making them unsuitable for complex professional tasks.

The core logic of this project is very practical:

Role Matrix: Breaking down business into dozens of specialized positions, including front-end engineers, penetration testers, product managers, and even marketing agents tailored for the Chinese market.

Lightweight Architecture: Using Markdown as a carrier, allowing global developers to quickly add new roles, like recently added Salesforce architects and Blender plugin developers, just like writing documents.

Low-Barrier Collaboration: Providing small and medium teams with a standardized "expert dictionary," significantly lowering the deployment barriers for multi-agent collaboration.

Piercing the "All-Round Illusion": Returning from Generalists to Specialists

This trend marks a profound transformation in the focus of AI applications. In 2026, as industries enter deeper implementation stages, they found that it's better to hire a row of "specialists" who work meticulously than to maintain a generalist who occasionally "talks nonsense."

The emergence of agency-agents essentially reflects the industry's collective recognition of the value of multi-agent collaboration:

Efficiency First: Prompt engineering has evolved from "chatting skills" into standardized "job descriptions."

Specialized Division of Labor: It proves that in the AI era, specialized division of labor remains the most effective way to improve productivity.

Growing Pains: From Geek Toy to Production Tool

Although it has attracted significant attention, agency-agents still faces real engineering challenges. For example, path conflicts in Windows environments, performance bottlenecks in large-scale parallel processing, and data isolation and permission management required for enterprise-level compliance. Currently, the development team is rapidly iterating based on community feedback, trying to standardize this "backyard team."