The Governance Layer
for the AI-Native Engineering Team.
We're building the control plane for AI coding agents — so every team can adopt them at full speed, without losing visibility, control, or trust.
We're not building better agents.
We're building the railway they run on.
AI agents are increasingly capable, but without structure they're loose cannons — hallucinating APIs, bypassing team conventions, and acting without institutional knowledge. The answer isn't to intercept every token. It's to govern at the layer above: control the context, observe the trace, and continuously evaluate the output.
Every agent. One control plane.
Every engineering team will run a fleet of AI agents within five years. Most don't have the tools to govern one, let alone a fleet. We're building the standard control plane so agent adoption can be bold — because the governance layer is already in place.
Governance, observability, and optimization — in one product.
Governance
All agent traffic routes through one gateway. Budget limits, model access controls, and guardrails enforced in real time — before code gets written, not after.
Observability
A local desktop app indexes every session across 12+ agents. Team dashboards aggregate cost, quality, and activity data across your entire fleet.
Optimization
ChooChoo generates and maintains context files, rules, and agent configs. When drift is detected, it opens a PR. Agents improve with every cycle.
A short history.
We built ChooChoo because every team we talked to had the same story.
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2026
Founded
Alex and Kim kept watching great engineers waste hours untangling what an AI agent actually did — and why. ChooChoo was founded to close the trust gap between agents and the teams that deploy them.
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Early 2026
First product ship
The ChooChoo CLI and local dashboard shipped as a free tool for individual developers. Structured agent traces became a first-class artifact — linked to PRs, searchable, and reviewable by the whole team.
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Today
The control plane
ChooChoo now spans a gateway, observability platform, eval harness, optimization engine, and GitHub + Linear integrations. One control plane for every agent on the team.
What we build, and how we build it.
Transparency
Every agent action is visible, searchable, and reviewable. No black boxes. If a decision was made, the trace is on the PR.
Traceability
Content-addressed, append-only trace records link every change to the agent, conversation, and model that produced it.
Context over cleverness
Agents don't need to be smarter. They need the right context. We invest in governance-as-code, not model wrappers.
Security-first
Security is not an add-on; it's the core of our product. We protect decision traces with the rigor you apply to production code.
Developer-first
The local CLI is free and will stay free. Every team should be able to govern their agents without a procurement cycle.
Built for scale
One gateway, one dashboard, one policy layer — for one agent or a fleet of hundreds. Governance that grows with the team.
Frequently asked questions.
Who is ChooChoo for?
How is ChooChoo different from an observability tool?
Which agents do you support?
Do you store my code?
Is there a free tier?
How do I get started?
How do I contact the team?
Recent posts.
Thoughts on AI agents, code review, and the future of software teams.
Introducing ChooChoo
We built ChooChoo because we kept watching great engineers waste hours untangling what an AI agent actually did — and why. Today we're sharing why we think agent traceability is the missing piece in modern software teams.
How AI Agents Change Code Review
Code review was designed for humans reviewing human-written code. When an agent authors a PR, the same process breaks down in subtle ways. Here's how we think about reviewing agent work differently.