# AI Agent Performance Auditor (`wintry_nutmeg/ai-agent-performance-auditor`) Actor

Gate AI-agent releases with reliability, quality, latency, and unit-economics regression checks. Compare current runs with a baseline and export CI-ready JSON.

- **URL**: https://apify.com/wintry\_nutmeg/ai-agent-performance-auditor.md
- **Developed by:** [Dries Vd](https://apify.com/wintry_nutmeg) (community)
- **Categories:**
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $250.00 / 1,000 ai agent performance audits

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## AI Agent Performance Auditor

Measure whether an AI agent is reliable, economical, and safe to promote to production. Supply observed runs from one release or model and optionally a baseline cohort. The Actor returns a structured health report for engineering, product, FinOps, and AI-operations teams.

### What you get

- Success and failure rates
- Cost per run and cost per successful run
- Revenue, gross profit, and gross margin when revenue data is supplied
- Average and p95 latency
- Average output-quality score
- Model and version cohort comparisons
- Regression detection against a baseline
- Prioritized operational recommendations

### Who this is for

- AI engineering teams evaluating a release before rollout
- Platform teams comparing models or agent versions
- FinOps teams monitoring cost per successful outcome
- AI product owners tracking reliability, quality, latency, and margin
- Automated CI or observability workflows that need predictable JSON

### Example input

```json
{
  "runs": [{
    "id": "run-101",
    "success": true,
    "costUsd": 0.04,
    "revenueUsd": 0.20,
    "latencyMs": 1200,
    "qualityScore": 92,
    "version": "1.1",
    "model": "model-a"
  }],
  "baselineRuns": [{
    "id": "run-001",
    "success": true,
    "costUsd": 0.06,
    "revenueUsd": 0.20,
    "latencyMs": 1700,
    "qualityScore": 84,
    "version": "1.0",
    "model": "model-a"
  }],
  "thresholds": {
    "successRateDropPoints": 5,
    "qualityDropPoints": 5,
    "latencyIncreasePercent": 20,
    "costPerSuccessIncreasePercent": 20,
    "marginDropPoints": 5
  }
}
```

Each run accepts `success` or `status`. Optional fields are `costUsd`, `revenueUsd`, `latencyMs`, `qualityScore` from 0–100, `version`, `model`, and `timestamp`.

### Output

One report is written to the default dataset. It includes a CI-friendly `releaseDecision` (`GO`, `REVIEW`, or `HOLD`), `healthScore`, `status`, aggregate `metrics`, `regressions`, `byVersion`, `byModel`, and `recommendations`. Export it as JSON, CSV, or Excel, or consume it through the Apify API.

### Pricing

The Actor charges one `agent-performance-audit` event per successfully generated report. Invalid input fails before a paid report is produced. Platform usage is included; use Apify's maximum-charge control for an additional run guardrail.

### Responsible use

Only submit operational data you are authorized to process. Do not include secrets, personal data, private prompts, or raw customer content. Metrics describe the supplied observations and do not guarantee future revenue or model quality.

# Actor input Schema

## `runs` (type: `array`):

1–1,000 run objects. Use success or status; optionally add costUsd, revenueUsd, latencyMs, qualityScore (0–100), version, model, and timestamp.

## `baselineRuns` (type: `array`):

Earlier release or model cohort used for regression detection.

## `thresholds` (type: `object`):

Override success/quality drop points and latency, cost, or margin thresholds.

## Actor input object example

```json
{
  "runs": [
    {
      "id": "run-101",
      "success": true,
      "costUsd": 0.04,
      "revenueUsd": 0.2,
      "latencyMs": 1200,
      "qualityScore": 92,
      "version": "1.1",
      "model": "model-a"
    },
    {
      "id": "run-102",
      "success": true,
      "costUsd": 0.05,
      "revenueUsd": 0.2,
      "latencyMs": 1350,
      "qualityScore": 89,
      "version": "1.1",
      "model": "model-a"
    },
    {
      "id": "run-103",
      "success": false,
      "costUsd": 0.03,
      "revenueUsd": 0,
      "latencyMs": 2400,
      "qualityScore": 55,
      "version": "1.1",
      "model": "model-a"
    }
  ],
  "baselineRuns": [
    {
      "id": "run-001",
      "success": true,
      "costUsd": 0.06,
      "revenueUsd": 0.2,
      "latencyMs": 1700,
      "qualityScore": 84,
      "version": "1.0",
      "model": "model-a"
    },
    {
      "id": "run-002",
      "success": false,
      "costUsd": 0.05,
      "revenueUsd": 0,
      "latencyMs": 2900,
      "qualityScore": 62,
      "version": "1.0",
      "model": "model-a"
    }
  ],
  "thresholds": {
    "successRateDropPoints": 5,
    "qualityDropPoints": 5,
    "latencyIncreasePercent": 20,
    "costPerSuccessIncreasePercent": 20,
    "marginDropPoints": 5
  }
}
```

# Actor output Schema

## `reports` (type: `string`):

Structured AI-agent health, economics, cohort, and regression reports.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("wintry_nutmeg/ai-agent-performance-auditor").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("wintry_nutmeg/ai-agent-performance-auditor").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call wintry_nutmeg/ai-agent-performance-auditor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,wintry_nutmeg/ai-agent-performance-auditor"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/w9sIaOeo3aupkTTdY/builds/SGyvTay8SsofOhkqZ/openapi.json