Mesa

Most people know Mesa today as an AI code review company. What fewer people know is that code review is not the product. It is the wedge.

The real ambition is larger. We want to build the collaboration system for swarms of coding agents and humans. GitHub for Agents.

This is the master plan.

The purpose

Software is becoming the primary instrument by which humanity shapes the world. It runs the systems that produce our energy, move our goods, settle our markets, and defend our societies.

The limiting factor is no longer ambition. It is our ability to build reliable, evolving systems at scale.

Mesa exists to raise that ceiling.

What must be created

If agents are going to write a meaningful share of the world’s code, we need more than a faster way to generate diffs.

We need a shared workspace where humans and machines can build large systems together, and where the work remains understandable, auditable, and controllable as it compounds.

That requires three things.

First, intelligence at the boundary where code becomes trusted. Tools like an AI code reviewer that can verify change, explain it, and catch failure modes before they ship.

Second, a code substrate that is native to machines. A headless code storage and versioning system designed for long running automated work, cheap branching, and machine authored history, with humans still able to interoperate.

Third, a human control plane. An AI first frontend where people can supervise fleets of agents, understand why changes happened, and decide what is allowed to move forward.

Why this has to be built in this order

Almost any new technology starts expensive, awkward, and niche. The path is to enter where the value is undeniable, then work down toward the deeper infrastructure.

So we start with intelligence tools like AI code review. That is where teams already feel pain, where trust is earned, and where we can learn how agents fail in the real world.

Then we build the substrate where agent code wants to live, because once agents are producing large volumes of change, the old assumptions in today’s tooling start to break.

Finally, we build the control plane, because at scale the bottleneck is not writing code. It is understanding and governance.

Each step is useful on its own. Each step makes the next inevitable.

Objections

People ask, "why not just use the existing stack?"

GitHub is excellent for human collaboration. But agents do not work like humans. They run continuously, explore in parallel, checkpoint constantly, and generate history at a volume that turns human oriented workflows into noise. If we want agents to be safe and productive at scale, the substrate has to treat machines as first class participants.

People ask, "why not just build better agents?"

Better models help, but they do not solve coordination. The hard part is not producing code. The hard part is turning change into trusted change, and keeping a system legible as thousands of automated workers modify it.

People ask, "why focus on control instead of simplicity?"

Because the software that matters most is not demo software. It is the software that cannot go down. The value comes from ambition with reliability. That requires verification, provenance, and governance, not just generation.

The master plan, summarized

Build an AI code reviewer
Use that to build agent native code storage
Use that to build a human control plane for supervising swarms

Don't tell anyone.