The KAIROS Substrate maintains a verified, CI-gated proof of correctness across all six safety decision paths. The test suite processes fifty-two scored scenarios in strict mode and returns a one hundred percent pass rate.
AI safety evaluation traditionally terminates at deployment. Researchers construct test sets, score outputs, and tune thresholds prior to shipping. Once in production, real inputs diverge from test conditions and the evaluation machinery ceases operation. Benchmarks measure static model behavior. Production AI systems operate as continuous, evolving environments where agent state changes with every action. A static test score holds no binding authority over an agent at runtime.
AnankeLabs builds the KAIROS Substrate to enforce this authority. The Substrate operates as a runtime safety layer positioned between an LLM and the external systems it affects. Every proposed action passes through this gate prior to execution. The Substrate evaluates the request against live engine state and returns a binding decision.
The engine models each agent as a physical system governed by two measurable parameters. Lambda defines the rate at which the agent pursues objectives. Gamma defines the structural margin it maintains against hazardous states. Both parameters evolve continuously with each action, and the Substrate reads them on every evaluation cycle.
This evaluation is entirely deterministic. Given identical inputs, the Substrate produces identical outputs. This mathematical rigidity makes correctness strictly verifiable. A deterministic system produces a specific output that engineers score, regression-test, and formally check.
The architecture defines six decision paths. All six currently pass. PASS clears the action for execution. REJECT_STATE fires when Lambda or Gamma parameters breach policy bounds. REJECT_ACTION fires when the proposed action fails a physics-based preview. REJECT_STALE_METRICS fires when telemetry age exceeds the trust window.
Two terminal gates enforce absolute boundaries. REJECT_BASIN_COLLAPSE fires when the engine projects total future topology collapse for the agent. REJECT_PARADOX fires upon multi-agent collision detection. Both gates trigger a non-overrideable halt. They permit no retry budget, no escalation path, and no operator override.
Each terminal gate validates through a policy preview branch and an action preview branch. Testing both branches verifies the final decision and the exact internal path that produced it. Every scenario scores on five strict dimensions: decision, reason code, assessment level, hazard gate status, and preview source. A single regression in any dimension fails the build. This harness executes continuously in CI.