AI Governance
When AI agents write and ship code, governance is what proves who did what, why it was allowed, and whether you can show it afterward. A policy document alone enforces nothing once an agent is running.
The problem
Three risks at the organization level, not the team level:
AI writes code no one can account for. For a regulated organization, a change nobody can explain is a non-starter.
On autopilot, agents always ship something that runs. Left to themselves they over-create and duplicate: it works today and rots tomorrow, and nobody chose that path or can answer for it.
AI produces plausible code faster than any review can keep up. Quality bolted on at the end never scales to agent throughput.
AI Governance
AI governance is the topic of the moment. The usual answer is a policy paper. But a document enforces nothing. Governance only works once it takes effect at runtime. That's exactly where it resides in our system: in the platform, which verifies and enforces every AI policy in real time. Sovereignty, Traceability, Control and Built-in Quality are seamlessly integrated into the operating model.
How beacon platform implements it
Sovereignty, Traceability, Control and Built-in Quality: the same operating model that runs every unit of work on the platform, not a separate governance layer bolted on top.
No lock-in: not to a cloud, not to a model. Your platform runs where you choose; your model is your own, on your keys.
Who did what, on which Spec, with which model. Every unit of work traceable, live in the product.
Human-in-the-loop by design: go, adjust, stop at every checkpoint. No black-box autopilot.
Spec-first, red-first. Quality is built into the system, not bolted on in review.
Questions
Every agent acts under its own identity, never a shared account. Two human control points frame the work, after the Spec and before merge, and a human integrator is the sole merger, so nothing ships without an explicit go.
Yes. Every unit of work is a Task with a full Activity Timeline: who did what, when, on which Spec, with which model, live in the product rather than a log you have to reconstruct.
No. BYOK throughout: model calls run on your own provider contract, never through us, with no bundled token quota. Deploy on-prem inside your own Perimeter, or Swiss-hosted. You choose.
Single sign-on is on request, set up as soon as your engagement needs it. We answer security questionnaires and vendor assessments on request today, and would pursue a certification such as ISO 27001 if a customer engagement required it, not before.
Guardrails, Spec Test Driven Development and a Definition of Done are enforced automatically on every change, so quality is a property of the system rather than a review step that gets skipped under pressure.
Explore more
Ship with AI agents and prove every step. License the AI-native Operating Model as a platform: sovereign, auditable, cost-aware, on-prem or Swiss-hosted.
Run beacon platform on-prem, as a single instance or an org-wide site license. In your perimeter, under your contract, with your own keys and audit trail.
How beacon platform handles data residency, model access, access control, architecture and compliance, in depth, for a security or architecture review.
Next step
For organizations deciding where to start. Eight questions, two minutes, based on the AI Adoption Maturity Model.
For organizations evaluating on-prem or Swiss-hosted deployment with their own infrastructure and keys.