Why AI Agents Need Their Own GitHub: The Case for Agent Infrastructure

In 2008, sharing code looked like this:

Email a zip file. Upload to an FTP server. Maybe use Subversion if your company had set it up. Keep the "real" version on your local machine and hope your hard drive didn't fail.

Open source existed, but contributing was hard. Every project had its own process. You had to know where to look, who to email, how to submit patches. Building on someone else's work required navigating a maze unique to each project.

Then GitHub changed everything — and it's easy to forget how radical that change was.

The insight behind GitHub wasn't "developers need a place to store files." That problem was already solved.

The insight was that code collaboration needed a shared vocabulary.

Fork. Clone. Branch. Commit. Push. Pull request. Merge.

Before GitHub, these concepts existed in various forms. After GitHub, they became universal. A developer in Tokyo and a developer in Toronto could collaborate without ever discussing process — because the process was built into the platform.

GitHub also created social signals for code:

• Stars showed what the community found valuable
• Forks showed what people were building on
• Profiles showed who was contributing and where
• Issues created a shared space for problems and solutions

Code became social. And that changed everything.

Plenty of code hosting existed before GitHub. SourceForge. Google Code. Self-hosted Subversion.

GitHub won because of network effects.

Developers went there because other developers were already there. You could find libraries, see who maintained them, check if they were actively developed. You could fork something interesting, and the original author could see your work.

Discovery happened by default. Collaboration happened by default. Building on each other's work happened by default.

This created a flywheel:

1. More developers joined because more projects were there
2. More projects moved because more developers were there
3. The cost of NOT being on GitHub grew every year

By 2015, asking "what's your GitHub?" was standard in engineering interviews. The platform had become infrastructure — invisible, essential, assumed.

Today, sharing AI agents looks a lot like sharing code in 2008.

Teams are building impressive agents — code reviewers, documentation generators, deployment automators, issue triagers. Real productivity gains. Genuinely useful tools.

But sharing them? Someone pastes a prompt in Slack. Someone commits a config file to a repo that nobody checks. Someone walks over to a colleague's desk and shows them the setup.

There's no shared vocabulary for agent collaboration. No universal way to fork an agent and specialize it. No social signals showing what's actually useful. No network effects pulling the ecosystem together.

Every team is solving the same problems independently. Every team is building from scratch.

When we talk about building GitHub for agents, we don't mean "a place to host prompt files." That would miss the point the same way "a place to host code files" missed what made GitHub transformative.

GitHub for agents means creating the vocabulary and network effects that make agent collaboration as natural as code collaboration became.

Forks and specialization

On GitHub, forking isn't copying — it's building on. You fork a library, make changes, maybe contribute back. The connection to the original remains visible.

Agents need the same thing. Take someone's code review agent, specialize it for your codebase, share your improvements. A visible chain of derivation that shows what came from where.

Social signals for quality

How do you know if an agent is good? Right now, you don't — until you try it.

GitHub solved this for code with stars, forks, commit activity, contributor counts. You could quickly assess whether a project was maintained, whether people found it useful, whether it was production-ready.

Agents need equivalent signals. Not just "someone shared this" but "teams are using this in production" and "this has been forked and improved 50 times" and "the community trusts this."

Profiles and reputation

On GitHub, your profile tells a story. What you've built. What you've contributed to. How active you are.

Agent builders need the same thing. A way to build reputation. A way to show what you've created. A way for organizations to identify who their agent experts are.

Discovery by default

The most powerful thing GitHub did was make discovery automatic. You didn't have to know where to look — the platform surfaced what was relevant.

Search for a library and find it. See related projects. Find alternatives. The ecosystem organized itself.

Agents need this desperately. Right now, discovering that someone already built what you need is pure luck.

GitHub's public repositories get the attention, but GitHub's enterprise business is built on private collaboration.

The same will be true for agents.

Most valuable agents won't be public. They'll be internal:

• Your security team's compliance checker
• Your platform team's deployment automation
• Your support team's issue classifier
• The documentation bot tuned to your codebase

This institutional knowledge is incredibly valuable — and incredibly fragile. When the person who built it leaves, it often leaves too.

GitHub for agents means capturing this knowledge in a form that persists. Organizational repositories. Team sharing. Internal discoverability. The ability to build on what colleagues have created without starting from scratch.

Social features alone aren't enough. GitHub worked because it combined community with solid infrastructure — hosting, version control, CI/CD, issue tracking.

Agent collaboration needs equivalent infrastructure:

Observability: When you fork an agent, you need to understand what it does. Every decision logged. Every tool call visible. No black boxes.

Testing: Before deploying an agent — especially one you didn't build yourself — you need to verify it works. Run it against scenarios. Catch edge cases. Build confidence.

Secure execution: Agents need access to do useful work. That access needs to be controlled, scoped, and auditable. The infrastructure has to make secure-by-default easy.

These aren't features — they're prerequisites. Without them, the social layer can't work. You can't confidently fork an agent you can't inspect. You can't trust an agent you can't test. You can't share an agent that requires copying credentials.

GitHub's network effects took years to build. In the early days, convincing projects to move was hard. The value wasn't obvious until the critical mass existed.

AI agents are at that early stage now. The ecosystem is fragmented. The network effects haven't kicked in. The flywheel hasn't started spinning.

But the conditions are right:

• Agent adoption is accelerating across engineering teams
• The pain of the current approach is becoming acute
• The productivity gains are real enough to justify investment

Someone is going to build the platform that captures these network effects. The ecosystem will consolidate around shared infrastructure, the same way it consolidated around GitHub for code.

We're building Guild.ai to be that foundation.

Not just hosting — but the vocabulary, the social signals, the network effects that make agent collaboration natural. Combined with the infrastructure that makes it safe: observability, testing, secure execution.

The goal is to make agent collaboration feel as obvious in five years as code collaboration feels today. A shared language. A community that builds on each other's work. Network effects that pull the ecosystem together.

We're early. The flywheel hasn't started. But it will.

If you want to be part of building it, we'd love to have you.

Join the waitlist →