# Counterfactual Agent Verifier (`firstrate/counterfactual-agent-verifier`) Actor

Compare matched baseline and challenger AI-agent runs before accepting an optimization. Terminal success and protected quality come first; lower cost, latency, token use, errors, or human interruptions cannot compensate for a material regression.

- **URL**: https://apify.com/firstrate/counterfactual-agent-verifier.md
- **Developed by:** [First Rate](https://apify.com/firstrate) (community)
- **Categories:** AI, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$10.00 / 1,000 comparison verifieds

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

## Counterfactual Agent Verifier

Compare matched **baseline vs challenger AI-agent results** before accepting an optimization.

Use this Actor when you changed an agent's model, toolset, context, retry policy, execution surface, routing, or other configuration and need to answer:

> Did the challenger preserve the terminal result and protected quality while actually improving cost, latency, token use, errors, or human interruptions?

### What it protects

A cheaper challenger is **not** classified as better when it regresses:

- terminal success,
- supplied quality beyond tolerance, or
- any supplied protected metric.

Unknown evidence stays unknown. Missing terminal evidence produces `insufficient_evidence`.

### Inputs

Provide `comparisons`, each containing a matched workload case with `baseline` and `challenger` objects. The most important field on both sides is `terminalSuccess`.

Optional evidence includes `quality`, `costUsd`, `latencyMs`, `inputTokens`, `outputTokens`, `errorCount`, `humanInterruptions`, and explicit `protectedMetrics`.

### Outputs

Each comparison is classified as one of:

- `regression`
- `candidate_improvement`
- `decision_equivalent_observed`
- `insufficient_evidence`

A batch can report `replaySetPass` only when the caller explicitly marks the workload representative and every supplied case is comparable with no observed regression. Even then, this Actor **never authorizes production promotion by itself**.

### Why this is different from a generic agent evaluator

This Actor does not generate subjective scores or run an LLM judge. It is a deterministic evidence witness for a narrow problem: **counterfactual verification of an already-proposed change on matched cases**.

It is designed to compose with trace auditors, retry analyzers, toolset pruners, routers, and CI systems.

### Epistemic boundary

This Actor compares the evidence you supply. It does not prove that two workloads are truly matched, infer missing quality, execute the replay, or prove business value. Cost savings cannot compensate for terminal or protected-value regressions.

# Actor input Schema

## `comparisons` (type: `array`):

One or more matched workload cases. Provide terminalSuccess for both sides; optional quality and efficiency metrics improve the comparison.

## `qualityTolerance` (type: `number`):

Absolute tolerated decrease before quality is classified as a regression.

## `representativeWorkload` (type: `boolean`):

Set true only when the supplied cases are genuinely representative of the deployment workload. This still does not authorize production promotion.

## Actor input object example

```json
{
  "comparisons": [
    {
      "caseId": "task-1",
      "baseline": {
        "terminalSuccess": true,
        "quality": 0.9,
        "costUsd": 0.05,
        "latencyMs": 1200,
        "inputTokens": 6000
      },
      "challenger": {
        "terminalSuccess": true,
        "quality": 0.9,
        "costUsd": 0.02,
        "latencyMs": 850,
        "inputTokens": 3200
      },
      "protectedMetrics": [
        {
          "name": "accuracy",
          "direction": "higher",
          "baseline": 0.95,
          "challenger": 0.95,
          "tolerance": 0.01
        }
      ]
    }
  ],
  "qualityTolerance": 0,
  "representativeWorkload": false
}
```

# Actor output Schema

## `results` (type: `string`):

Machine-readable per-case counterfactual verification results.

## `summary` (type: `string`):

Aggregate regression, candidate-improvement, and replay-set evidence.

# 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("firstrate/counterfactual-agent-verifier").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("firstrate/counterfactual-agent-verifier").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 firstrate/counterfactual-agent-verifier --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,firstrate/counterfactual-agent-verifier"
        }
    }
}

```

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/q31WQSvCAJd9rPQ2q/builds/MnXd3CJHlEA2iKZzE/openapi.json