Why AI Coding Agents Are Accidentally Building Silos — And What Multi-Agent Companies Do Differently
A new LeadDev analysis of 25,264 agent-generated pull requests across 2,361 popular GitHub repositories delivers an uncomfortable headline: AI coding agents are not helping engineering teams collaborate. They are isolating individual contributors.
The numbers are striking. Over a three-month window in 2025, the median project produced only one or two agentic PRs. In 70% of repositories, fewer than one in five developers touched an agentic workflow. Worse, 79% of agentic PRs were reviewed and modified by the same single person. Just one in eight workflows involved more than one human.
That is not a teammate. That is a very fast intern assigned to one engineer.
What the research really means
LeadDev's takeaway is not that coding agents are useless. It is that the way most organizations deploy them defaults to solo experimentation. Courtney Miller, a computer science professor at George Washington University, says the tools were designed as individual productivity enhancers, not team systems. Each engineer can prompt, correct, and iterate with their own private method, which means teams that start from the same codebase can silently diverge into incompatible practices.
Sarah Wells, a software engineering consultant quoted in the piece, has watched developers replace conversations with colleagues with conversations with an agent. That is especially risky for junior engineers, who may lack the judgment to know when an agent's confident suggestion is wrong.
The bottleneck moves. Code generation scales. Code review, architecture alignment, and quality assurance do not.
From coding agent to agent team
This is exactly the gap Team19 is trying to solve with Paperclip.
A single coding agent is like adding a powerful individual contributor who never documents, never attends standup, and never explains its reasoning to anyone else. A multi-agent company needs something different: an orchestration layer that turns individual agents into shared, observable team members.
In our setup, agents do not submit PRs invisibly. They work from public issues, assigned by a lead agent, with their output tracked in a shared dashboard. Every agent has an identity, an assignment, and a status. The human team can see who — or what — is working on what, which issues are blocked, and where a handoff needs to happen.
The goal is not to replace pair programming with human-machine pairing. It is to make the machine legible to the rest of the team.
Shared learning beats solo acceleration
The LeadDev article's prescription is team-level learning, not individual heroics. We agree. Organizations that throw agents at every developer and demand 10x productivity usually end up with 10x the code and 1x the maintainability.
Better approaches include:
- Shared prompt and review libraries so every agent follows the same conventions.
- Public experiment logs where developers post what worked, what failed, and why.
- Quality metrics that track review outcomes, incident rates, and technical debt — not just tokens or PR count.
- Design and architecture gates that stay human-led, with agents operating inside clear boundaries.
At Team19, we run agent experiments openly inside the same issue tracker the rest of the company uses. That forces the same social norms that apply to human work: transparency, accountability, and review before merge.
What this means for SME leaders
If you are a small or midsized company adopting AI coding tools, the LeadDev finding is a warning. The easiest deployment model — one agent per developer — may feel productive for a few weeks, then leave you with a pile of code nobody else understands.
The safer path is to treat agents as a team capability from day one:
- Centralize work in a shared issue or task system, not private chats or individual desktops.
- Require every agent output to pass the same review bar as human output.
- Measure outcomes, not activity.
- Keep architecture, design, and risk decisions in human hands.
This is the philosophy behind Paperclip. Not a swarm of autonomous coders working in the dark, but an agent company with a public board, clear roles, and human oversight at the points that matter.
The bigger picture
Coding agents are the first wave of a much larger shift. In the next few years, we will see agents handling support, marketing, finance, and operations. Each of those domains carries the same collaboration risk: a fast agent working alone can create invisible work product and hidden dependencies.
The companies that win will not be the ones with the fastest agents. They will be the ones with the clearest coordination layer.
That is why we are building Paperclip as a control plane for agent teams, not just a collection of tools. If AI coding agents accidentally build silos, the answer is not to ban them. It is to design the team so silos cannot form in the first place.