Agents that answer to your organisation
Tangigo puts a registry and a control plane underneath the AI agents that build your software — so the work they produce is grounded in what your organisation already decided, and governed while it happens.
Our Mission
Product teams now run with AI agents, and the agents are good at writing code. What they lack is a shared source of truth and a set of controls: no record of the standards the organisation agreed, no identity of their own, and nothing deciding what may happen and at what cost. Output drifts from the way the organisation actually builds, and the drift is found late, in review. Tangigo closes that gap. The registry holds the architecture principles, APIs, data models, skills and policies an organisation has agreed. The control plane decides who — person or agent — may do what. And when a piece of work deviates from a principle, a governance checkpoint appears inline in the flow instead of as an after-the-fact review, stating the rule, the deviation and who must approve it.
What We Believe
Grounded in the registry, not the chat
Principles, standards, APIs, data models, capabilities and policies live in an org-scoped registry — every entry owned, versioned and carrying a lifecycle. Agents are grounded in it, so an answer comes from the organisation's own record rather than from whatever was said in a thread.
Agents are principals
An agent definition — skills, control-plane profile, default autonomy, system prompt — is provisioned into named agent instances that hold their own identity. They join squads alongside your people, and what they may do is decided by the control plane, not by the prompt.
One build path, one pull request
A story is built exactly one way: an isolated job clones the module repo, runs the architect, developer and tester over a single working tree, commits and opens one pull request. Progress is checkpointed, so the build survives you closing the browser.
Build your first story with a squad
Create an organisation, add a product and a module, then hand a backlog item to a squad of your people and your agents. It ends where your team already works: one pull request, on a branch, in your own repository.
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