TRIBUNAL — Adversarial Validation Engine

Docket · In re

The Case for TRIBUNAL

Your validators have no playbook for agents.

An AI fraud-triage agent reviews a $312,000 wire. Its own retrieval flags a 74% match to a business-email-compromise typology. Then it downgrades the alert, never mentions that signal again, releases the wire — and writes its justification after the money moved. Your model risk team knows how to validate a credit model. Nothing in the traditional playbook covers a system that plans, calls tools, and self-corrects.

TRIBUNAL is an adversarial examiner — an AI that cross-examines other AIs. It challenges every decision the way a hostile bank examiner would, and turns the record into validation evidence.

Examine

Every step of the agent's decision trace is placed under oath. Reasoning holds, or reasoning breaks — with the specific challenge on record.

Evidence

Findings map to the SR 11-7 pillars — conceptual soundness, ongoing monitoring, outcomes analysis — with severity, remediation, and a hash-chained, tamper-evident audit trail.

Closure

Apply controls, re-examine, and document the verdict journey — FAIL to PASS — in the language an MRA remediation memo requires.

Detection isn't the product. Certified closure is. Run the docket above: watch the examiner break the agent's reasoning, then watch the remediated build survive re-examination with residual risk graded honestly.

TRIBUNAL is currently selecting one institution to pilot adversarial validation on a single agent — synthetic or sanitized traces, four to six weeks, examiner-ready evidence pack as the deliverable.

Request the pilot conversation