---
name: undominated-agent-consensus-cost-audit
description: "Audits token costs and accuracy ROI of multi-agent consensus, majority voting, and debate protocols. Recomputed fields must match the other inputs. A pass is not a compliance, safety, or price verdict."
license: MIT
metadata:
  author: Undominated.ai
  version: "1.1.0"
---

# Agent consensus cost audit

## Description
Audits token costs and accuracy ROI of multi-agent consensus, majority voting, and debate protocols.

## Category
`pricing`

## Tags
`pricing`, `consensus`, `majority-voting`, `economics`, `multi-agent`

## When to Use
Audit multi-agent deliberation systems to verify that consensus accuracy gains justify the cost multiplier.

## Operational Limitations
Evaluates accuracy deltas against cost multipliers; does not conduct voting rounds.

## Verification Script
Run the deterministic verification checker:
```bash
python3 scripts/check.py examples/synthetic.json
```
Exit codes:
- `0`: Check passed and thresholds satisfied.
- `1`: Threshold breach or operational violation detected.
- `2`: Schema error or invalid usage.

## What the checker recomputes

These declared fields must match the other inputs. The tolerance is 0.02, or 0.1% of the recomputed value when that is larger.

- `accuracy_gain_percent` = pct_points(consensus_accuracy, single_agent_accuracy)

The original threshold checks still apply:

- `accuracy_gain_percent` must not be < `min_roi_gain`.

Every other declared field is type-checked. A negative value is rejected except for deltas, drifts, and gains. A type check does not, by itself, prove the measurement.

`population` and `observedAt` are required. The bundled examples are synthetic fixtures, not market measurements.

```bash
python3 scripts/check.py examples/synthetic.json
python3 scripts/check.py examples/contradiction.json
python3 scripts/check.py examples/broken.json
```

Exit 0 is a pass, exit 1 is a review, and exit 2 is invalid input. A pass does not certify a provider, a regulation, a release, or a price.