> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pecta.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# How Pecta agent reputation scores are computed

> Every agent_id gets a portable 0–1000 score computed from pass rate, latency, streaks, and volume — updated over a rolling window of 500 evaluations.

Every agent evaluated by Pecta accumulates a reputation score tied to its `agent_id`. The score is a portable 0–1000 integer stored centrally, which means it travels with the agent across every platform and integration mode — whether the agent is gated via the SDK, the proxy, or the REST API. A single score reflects all of an agent's evaluations regardless of where they originated.

## Scoring formula

The score is a weighted sum of four components, each normalised to its maximum contribution:

```
Score = (pass_rate × 400) + (latency_score × 250) + (streak × 200) + (volume × 150)
```

| Component       | Weight | Description                                                                                      |
| --------------- | ------ | ------------------------------------------------------------------------------------------------ |
| `pass_rate`     | 400    | Fraction of evaluations that passed, in the rolling window. `1.0` = all pass.                    |
| `latency_score` | 250    | Normalised inverse of average latency. Faster agents score higher.                               |
| `streak`        | 200    | Rewards consecutive passing evaluations. A long unbroken streak raises the score.                |
| `volume`        | 150    | Rewards consistent usage. More evaluations in the window increases this component up to its cap. |

The maximum possible score is 1000. A brand-new agent with perfect evaluations climbs toward 1000 as it accumulates volume.

## Lifecycle states

The score goes through four lifecycle states as evaluations accumulate:

| State         | Condition          | Meaning                                                                           |
| ------------- | ------------------ | --------------------------------------------------------------------------------- |
| `new`         | 0 evaluations      | Agent has never been evaluated.                                                   |
| `calibrating` | 1–49 evaluations   | Score is being established; shown as `N/50` in the dashboard.                     |
| `active`      | 50–499 evaluations | Score is statistically reliable and fully displayed.                              |
| `mature`      | 500+ evaluations   | Rolling window is fully saturated; oldest evaluations age out as new ones arrive. |

<Note>
  The score only becomes visible to external consumers at 50 evaluations. Below that threshold the data is insufficient for a statistically reliable signal, so the dashboard displays the calibration progress instead of a raw number.
</Note>

## Rolling window

Reputation is computed over the **last 500 evaluations**. When a new evaluation arrives and the window is full, the oldest entry is evicted. This means an agent can recover from a bad period: sustained good behaviour will eventually push earlier failures out of the window.

## Portability

The score is stored centrally under a stable `agent_id` per organization. Any integration mode that uses the same `agent_id` writes to the same window:

* An SDK evaluation in your Node.js service
* A proxy evaluation from Claude Desktop on a developer's laptop
* A REST API evaluation from your Python test suite

All three update the same rolling window and the same score. You control the `agent_id` string, so you can segment agents as finely as you need (e.g. `dsp-bidder-prod` vs `dsp-bidder-staging`).

## Reading the score

The REST API returns the current reputation inline on every `/v1/evaluate` response:

```json theme={null}
{
  "evaluation_id": "v7k2mQpXtR9fNwJz",
  "passed": true,
  "reputation": {
    "score": 812,
    "lifecycle": "active",
    "eval_count": 124
  }
}
```

You can also fetch the score directly:

```bash theme={null}
curl https://api.pecta.ai/v1/reputation/research-bot-v2 \
  -H "Authorization: Bearer $PECTA_API_KEY"
```

```json theme={null}
{
  "agent_id": "research-bot-v2",
  "score": 812,
  "lifecycle": "active",
  "eval_count": 124,
  "window_size": 500
}
```

Scores are served with low latency and reflect evaluations ingested within seconds.