How Pecta agent reputation scores are computed

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.

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.

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:

{
  "evaluation_id": "v7k2mQpXtR9fNwJz",
  "passed": true,
  "reputation": {
    "score": 812,
    "lifecycle": "active",
    "eval_count": 124
  }
}

You can also fetch the score directly:

curl https://api.pecta.ai/v1/reputation/research-bot-v2 \
  -H "Authorization: Bearer $PECTA_API_KEY"

{
  "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.

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Core Concepts

Gates

RTB Gates

Dashboard

How Pecta agent reputation scores are computed

Scoring formula

Lifecycle states

Rolling window

Portability

Reading the score

​Scoring formula

​Lifecycle states

​Rolling window

​Portability

​Reading the score

Scoring formula

Lifecycle states

Rolling window

Portability

Reading the score