External expertise, safely deployed.
Build an AI agent once. Point it at real work. Deploy the same method into a client's private context — without your method or their data ever leaking.
How it works
A method-owner builds the agent. A context-owner provides the data. GaugeWright runs the two together inside a controlled boundary that neither side can cross.
Build the method
Define an agent — its instructions, skills, and tools — in your own library. Refine it in an edit chat until it does the job.
Place it on the work
Install the agent onto a project and give it tasks. Each run works in an isolated sandbox and hands you a diff to keep or discard.
Deploy without leaking
Package the method and deploy it against a client's private context. The method stays protected; the data stays protected; every release is auditable.
Why GaugeWright
One substrate that scales from a personal project to a governed, cross-party deployment — governance is added, never re-architected.
Agentic workbench
Point an agent at your files, give it a task, review the diff. It remembers context across turns within a chat — no re-priming.
Package & reuse
Build an agent once and deploy it repeatedly on new contexts without shipping your source or touching theirs.
Strict protection
Method and data are protected resources. Nothing is read, exported, or revealed without explicit admission. Fail-closed by default.
Local & offline-first
Core work runs entirely on your machine. No cloud dependency to get started; federation is opt-in.
Audit & rollback
Every run is a commit plus an admitted event. Full history, reversible changes, integrity you can verify.
Progressive governance
The same architecture spans solo work, team collaboration, and governed deployments across machines and parties.