The complete runtime path — from uncertain upstream input to deterministic downstream execution control. One product. One boundary. Verifiable evidence trail.
AI proposes. Integrity stabilizes. MGOS authorizes. Evidence proves.
Current guardrails are probabilistic, model-internal, and unauditable. Between proposal and execution, the authorization boundary is often fragmented, implicit, or unverifiable.
The AI is brilliant — it plans, optimizes, learns. But it also hallucinates, contradicts itself, and has no concept of consequence. Between the proposal and the robot arm, you need something that never guesses.
Data arrives from everywhere — sensors, models, databases. The Integrity Engine doesn't decide. It asks one question: can these inputs be stabilized without collapsing genuine conflict into false consistency?
If two sensors disagree, it preserves the conflict as a signal. Only a stabilized state moves downstream.
It receives the stabilized state and answers exactly one question: is execution allowed?
Three outcomes. No fourth option. No "maybe." No inference. The same stabilized state under the same policy always produces the same result.
Every decision produces a cryptographic receipt. SHA-256 hash, manifest, timestamp. Cryptographically verifiable. Tamper-evident. If someone asks in a year why the robot stopped — the receipt and evidence trail exist.
The human operator sees everything. Every decision, every conflict, every receipt.
Run tests. Inspect decisions. Export evidence. A runtime without operator visibility is operational risk.
Patent pending (PL/US) | Core logic Lean 4 verified | Deterministic | Fail-safe
Core authorization logic proved in Lean 4:
Implementation layer:
Black-box test suites:
MGOS RUNTIME STACK
eval@mgos.io