Systems
Put AI to work, under control.
Takes a promising capability to governed production: integrated, permissioned, evaluated, monitored, with human control and defined failure handling — and economics that hold at real volume.
When this fits
When this system is appropriate
- A prototype works but cannot yet be trusted in the operation.
- Autonomy must be bounded by the consequence of being wrong.
- The capability needs evaluation, monitoring, and recovery to ship.
- Cost, latency, and quality at real volume must be known.
Architecture
Architecture and boundaries
The model or agent runs inside a control boundary: scoped permissions, the tools and retrieval it is allowed to use, evaluation before and during production, monitoring, and recovery. Authority is proportional to consequence — from advice to bounded autonomy — and never exceeds what evidence supports.
Control boundary
Text description of this diagram
The model or agent sits inside a control boundary and is wrapped by the governance it needs to run safely: scoped permissions, the tools and retrieval it is allowed to use, evaluation, monitoring, and recovery.
Consequential actions do not leave the boundary on their own — they pass a human approval gate. Authority is proportional to consequence.
Autonomy proportional to consequence
- Advice only The system informs; a person decides and acts.
- Proposed action The system proposes; a person reviews and executes.
- Approval-gated action The system prepares, and acts only after human approval.
- Bounded autonomous action The system acts within explicit limits, monitored, with recovery.
- Continuous autonomy Only for observable, reversible, low-consequence behavior with proven recovery.
Human roles
Human roles and failure handling
Humans stay responsible for consequential decisions. The system proposes or acts only within its bounded authority, gates consequential steps for approval, and has a defined, tested path when it fails or is unsure.
Evaluation
Evaluation and acceptance
Acceptance requires an evaluation set, agreed quality and safety bars, and demonstrated recovery — measured before production and monitored continuously. Nothing ships on a demonstration alone.
Deployment
Deployment and ownership
Runs in your environment with explicit data, identity, and permission boundaries. The system, its evaluations, and its audit history are yours.
Evidence & engagement
Evidence and engagement
This domain most directly resolves ai works in the demo, not in the operation . The published proof is Governed AI and agent blueprint, held to the standard set out on the evidence pages.
An engagement begins by describing the challenge — the mandate intake structures the target operation and control model before any build.
Engagement
Begin with what must change.
If a promising capability is stuck outside production, the first step is to define the control boundary it would need to cross.
Or submit an RFP, or request an NDA first.