Candidate outputs
Multiple agents or models generate structured candidate outputs.
Technology
Control the path from inference to action.
MIRAGE introduces a runtime control layer for AI decisions. It detects material conflict, compiles a controlled execution scope, selectively routes uncertainty, verifies model independence where required and governs release.
MIRAGE runtime architecture
Public system view
Controlled scope
Material disagreement is formalised into a verifiable, authorised re-evaluation scope.
Targeted validation
Only the authorised conflict dimensions are escalated to additional validation.
Controlled output
The downstream output can be released, annotated, quarantined or held uncertain according to validated conditions.
Architecture principles
Designed for non-deterministic AI.
The public architecture exposes the protected control concepts at system level without publishing production thresholds, calibration logic or operational heuristics.
Model agnostic
MIRAGE is designed to operate across different model providers and model classes.
Selective by design
Conflict does not trigger a blind full re-run. The uncertain scope is isolated and routed selectively.
Independent & auditable
Validation can enforce model-independence policies while relevant executions retain a replayable, inspectable audit trail.
Why it matters
A detected conflict changes execution.
MIRAGE is not a passive comparison layer. Conflict can activate a different, constrained runtime branch before release.
Detect
Identify material disagreement across models or agents.
Constrain
Turn the disagreement into a controlled, verifiable execution scope.
Route
Re-evaluate only the uncertain dimensions through additional models, evidence or human review when required.
Release
Govern whether the final output proceeds, remains uncertain, is annotated or requires escalation.
Protected execution logic
Visible architecture. Proprietary implementation.
The public layer explains the technical system. MIRAGE openly communicates the control architecture that defines its role: conflict handling, controlled scope, selective validation, model-independence checks, release gating and auditability.
Production thresholds, calibration methods, routing policies, optimisation logic and proprietary heuristics remain confidential.