VERA Operator — Multi-System AI Control Plane

The project now includes a working, private operations-console prototype for bounded worker tasks, saved-result review, and source-linked project activity. This extends the earlier supervisory design below; it does not turn the historical replay score into proof of unattended reliability.

Defined task inputs and explicit result states.

A review flow that distinguishes a saved result from an accepted result.

Activity records with source provenance, coverage limits, and correction handling.

Responsive desktop and mobile interfaces.

A clear distinction between refreshing displayed records and checking an upstream source.

Iteration has included automated checks of console behavior and browser-level desktop/mobile review. The operating model keeps evidence, access failures, pending review, and deployment status distinct rather than flattening them into a reassuring “done.”

Unattended reliability and scheduled source collection remain under evaluation. Access depends on the tools and permissions available to each runner. Whole-account chat monitoring is not established, and a dashboard refresh is not evidence of new upstream activity.

I led problem framing, workflow and interface design, constraints, evaluation standards, failure diagnosis, and revision decisions. AI tools assisted implementation and analysis under that direction.

The transferable client value is a process that is easier to inspect, review, and hand off before automation is expanded.

The following documents the earlier design and validation, not a new performance benchmark. The original cover illustrates that shadow-governance phase.

Specialist AI systems can each produce plausible answers while still disagreeing with one another, using stale state, missing decision-critical unknowns, or quietly crossing authority boundaries.

VERA Operator is a supervisory control plane that evaluates specialist-system outputs before consequential decisions are made. It standardizes what each specialist hands upward, checks evidence and source state, identifies conflicts, and returns bounded outcomes such as PASS, BLOCK, ESCALATE, RESEARCH, CONFLICT, or WAIT.

The architecture keeps specialist systems separate rather than merging their state. Consequential authority remains human-controlled, and the Operator does not grant itself execution rights.

Key design features include:

standardized specialist packets for evidence, assumptions, unknowns, confidence, requested authority, and requested state changes

explicit project and source-state isolation

decision-critical unknown promotion

lowest-sufficient-authority / escalation-economy rules

regression testing when a governance defect is found

shadow-mode validation before any later authority expansion is considered

The first blind historical replay used 10 cases and scored 88/90 (97.8%). The only deductions exposed a specific calibration defect: one decision-critical unknown was not promoted formally, and one case escalated unnecessarily.

I converted those failures into reusable governance rules, then ran a focused clean-context regression. The repaired version passed with the intended outcome: RESEARCH, Decision-Critical Unknown = YES, and Human Approval Required Now = NO.

Phase B live-shadow evaluation is active under read-only authority. A later live-routing test exposed an infrastructure problem: standing authorization alone did not create a reliable invocation hook. I redesigned the routing path around a synchronous pre-decision capture step and preserved the failed attempt as infrastructure evidence rather than counting it as a clean benchmark case.

Fresh-session routing validation remains the next gate. No trade execution, specialist-canon mutation, money movement, or cross-account synchronization authority is granted.

The project demonstrates a way to make multi-agent AI systems more governable: specialists can remain useful and independent while a separate supervisory layer checks conflicts, evidence quality, uncertainty, and authority before action.