> For the complete documentation index, see [llms.txt](https://docs.amesa.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.amesa.com/evaluate-performance/analyze-data.md).

# Analyze Data

## Benchmark Testing and Data Generation

After training a multi-agent system in AMESA, the system automatically runs a series of standardized tests to evaluate its performance. This benchmarking process:

1. Places the system in controlled testing environments
2. Records detailed metrics at each step of operation
3. Aggregates results to provide comprehensive performance statistics

The output of this testing process is compiled into a structured benchmark.json file, which contains rich performance data that can be analyzed to assess effectiveness, identify improvement opportunities, and compare different design approaches. This file is a performance record and a valuable analytics resource for optimizing your agentic systems.

## Downloading Benchmark Artifacts

To download benchmark data for further analysis:

1. Navigate to the "Training Sessions" page
2. Click the artifacts dropdown in the top right page of a trained system
3. Select "Benchmark"
4. The benchmark.json file will be saved to your local machine

<figure><img src="/files/7tQRx2j4gdo7N2kwXhCb" alt=""><figcaption></figcaption></figure>

## Understanding the Benchmark.json File

The benchmark.json file contains structured data about the performance of a trained agent system. Here's how to interpret this file:

### File Structure

```
{
  "skill-name": {
    "scenario-0": {
      "scenario_data": { ... },
      "episode-0": [ ... ],
      "aggregate": { ... }
    }
  }
}
```

### Key Components

**Scenario Data**: Contains reference values for the scenario:

```
"scenario_data": {
  "sensor_one": {"data": 8.57, "type": "is_equal"}, 
  "sensor_two": {"data": 373, "type": "is_equal"}
}
```

**Episode Data**: Array of state-action pairs showing how the agent performed in each step:

```
[
  {
    "state": "{'sensor_one': array([311.2639], dtype=float32), ...}",
    "action": "[-1.253192]",
    "teacher_reward": 1.0,
    "teacher_success": false,
    "teacher_terminal": null
  },
  ...
]
```

**Aggregate Statistics**: Summary statistics for the entire benchmark:

```
"aggregate": {
  "mean": { ... },
  "medians": { ... },
  "std_dev": { ... },
  "max": { ... },
  "min": { ... }
}
```


---

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