Unauthorized execution
AI-generated intent becomes action without matching authority, policy, or decision rights.
AI EXECUTION CONTROL
Creation must become worthy of the world it shapes.
LUXION builds execution-control infrastructure for consequential AI agents. Guardian Runtime checks whether proposed actions are evidenced, authorized, and controlled before they execute.
GUARDIAN IN ACTION
A proposed action becomes a governed decision before it becomes consequence.
Representative controlled-environment trace using Guardian's implemented decision vocabulary and evidence model. This is not production authorization or a customer case study.
- Technical preview
- Pre-execution evaluation
- Repair and escalation
- Replayable responsibility record
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Proposed action
Transmit a customer-data export to an external destination.
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Evidence and authority
Destination authority is not established, the requested data scope exceeds the stated purpose, and named review is required.
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Guardian decision
REPAIR
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Intervention
Restrict the exported fields, route to an authorized destination, and request the required human authorization.
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Responsibility record
Preserve the proposed action, evidence references, authority state, policy context, decision, repair conditions, and trace identity.
Current status: Guardian Runtime — technical preview and bounded evaluation product. Controlled-environment mechanisms and replay artifacts; canonical release-bound public evidence package in preparation.
Exact artifacts, methods, reproducibility status, and limitations are maintained through the Guardian evidence surface and LUXION Evidence & Scope.
CURRENT PRODUCT
Guardian Runtime
Guardian Runtime checks proposed AI actions before tools, data, workflows, or external systems are affected.
It checks evidence, authority, and policy before execution. It can admit, repair, defer, escalate, or deny—and preserve a replayable record.
Built for consequential AI workflows
Guardian is most relevant when an agent can affect external systems and its action can be checked before commit.
BEST FIT
- External consequence
- Defined authority
- Representable evidence
- Measurable failure cost
PRIMARY TEAMS
- AI platform and engineering teams
- AI security and architecture teams
- Operational risk, governance, and assurance teams
EXAMPLE WORKFLOWS
- sensitive data transmission
- privileged IT or security operations
- code and infrastructure modification
- financial-operation preparation
- customer-account actions
- regulated document and decision workflows
MISSION
Guardian governs consequential AI actions today.
LUXION is building the responsibility infrastructure for increasingly powerful intelligent creation.
Preserve legitimate objectives. Repair inadmissible paths.
Guardian design-partner pathway
A bounded pilot starts with one consequential agent workflow, one defined action boundary, and explicit success criteria. Guardian begins in observation or shadow mode before any selective enforcement decision.
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Select one workflow
Choose one agent-enabled workflow and one defined class of consequential actions with named owners and measurable failure cost.
Review pilot scope -
Map authority and evidence
Define decision rights, policy conditions, evidence sources, escalation paths, and the boundary between autonomous and human-authorized action.
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Run observation and shadow mode
Record proposals and compare Guardian decisions against controlled scenarios and representative workflow traffic before enforcement.
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Review evidence and decide
Measure control quality, legitimate-action preservation, latency, audit completeness, and unresolved risk before any selective enforcement decision.
Pilot outputs support fit assessment and deployment design. They do not constitute production clearance, safety certification, regulatory approval, or a guarantee that every unsafe action will be detected.
Evaluate one consequential workflow
Start with one agent workflow, one action boundary, and clear evidence and authority conditions.
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