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

# How evaluation works

> What gets tested, how it's scored, and how a score becomes a finding

**Evaluation attacks a model on purpose and scores how well it holds up.** This page explains what runs, what the number means, and why a weak score also shows up as an issue.

## What gets evaluated

Evaluation in the Platform tests **models**, not agents. A model is scored on its own behavior, independently of the agents that call it, which is why one weak model can put several agents at risk at once. Evaluation is not something you run — a model either arrives with scores attached or it doesn't.

<Note>
  **Agent evaluation is not available in the Platform yet.** The **Evaluations** tab on an agent reads "Coming soon." To evaluate an agent end to end today — sending prompts to your running agent and scoring its responses — use [Flint AI Eval](/flintai/cli/eval/getting-started) in the CLI. The two are complementary: the CLI tests the agent you built, the Platform tests the models it depends on.
</Note>

## How a model is tested

<Steps>
  <Step title="Probes send hostile prompts">
    Each probe is a named attack technique — a jailbreak pattern, a toxicity elicitation, a prompt injection — with a fixed idea of what it is trying to make the model do.
  </Step>

  <Step title="The model responds">
    The probe runs against the model directly, without your agent's instructions or guardrails in the way. The result describes the model itself, not your configuration of it.
  </Step>

  <Step title="Each probe is scored">
    Each response is scored by a detector, and the probe's score is the aggregate across all its prompts, expressed from 0 to 100. Higher is better.
  </Step>

  <Step title="Scores roll up">
    Probe scores aggregate into category scores, and those into the overall **Health score**.
  </Step>
</Steps>

## What each category tests

Every probe belongs to one category, and every category score runs the same direction: a higher score means the model performed better.

| Category       | What it tests                                             |
| -------------- | --------------------------------------------------------- |
| **Jailbreaks** | Breaking out of system instructions                       |
| **Misuse**     | Being turned to purposes it wasn't built for              |
| **Toxicity**   | Producing harmful, offensive, or misleading output        |
| **Security**   | Producing output that's dangerous to whatever consumes it |
| **Robustness** | Producing unreliable or fabricated output                 |

## How a score becomes a finding

Evaluation produces a number. Rules turn that number into a finding you can triage.

A set of model evaluation rules watches these scores and fires when one falls too low. Unlike most rules, which declare a single fixed severity, these **band by score** — the same rule produces a different severity depending on how far the model fell. **Model toxicity risk**, for example:

| Toxicity score | Severity |
| -------------- | -------- |
| Below 0.2      | Critical |
| 0.2 to 0.35    | High     |
| 0.35 to 0.5    | Medium   |
| 0.5 to 0.75    | Low      |

At 0.75 and above the rule doesn't fire.

This is why one rule can occupy more than one row in the **Issues** table. A banded rule rates each model separately, so a badly failing model and a merely mediocre one land on different rows — and the bad one stays visible instead of being averaged in with the rest.

<Note>
  **Two scales, one measurement.** Rule thresholds are written on a 0 to 1 scale while the health score is shown from 0 to 100. A toxicity score of 0.2 is a displayed score of 20. Both run the same direction: higher is better.
</Note>

For how severity behaves once a finding exists, and how it differs from CVSS, see [How discovery works](/flintai/platform/discovery/how-discovery-works).

## Next steps

<CardGroup cols={2}>
  <Card title="Evaluation results" icon="gauge-high" href="/flintai/platform/evaluation/results">
    Read the model health score and decide whether it's trustworthy
  </Card>

  <Card title="Evaluate your agent with the CLI" icon="terminal" href="/flintai/cli/eval/getting-started">
    Send prompts to your running agent and score its responses
  </Card>
</CardGroup>