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

# Verifiers

> Programmatic and rubric-based evaluation of agent behavior. 

For each task, Collinear’s **Verifier Engine** generates two complementary sets of verifiers that together cover the full evaluation surface:

## Programmatic Verifiers

Programmatic verifiers inspect the playground state directly. They compare before/after snapshots of the playground to confirm the agent made the correct changes.

**Example:** "Did the agent send an email to the correct recipient?" is answered by querying the email tool server's state and reviewing the state diff.

Programmatic verifiers are deterministic — given the same playground state, they always produce the same result. Use them for objective, checkable criteria.

## Rubric-based Reward Models

Rubric-based verifiers use reward models to evaluate the agent's actions against a scoring rubric. The judge reviews the agent's full trace and assigns a reward score.

**Example:** "Did the agent communicate professionally?" is evaluated by an LLM reviewing the conversation against a rubric defining professional communication.

Rubric-based verifiers are useful for:

* Subjective quality criteria (tone, clarity, helpfulness)
* Multi-step reasoning evaluation
* Cases where the "correct" answer depends on judgment

## How Verifiers Produce Rewards

Both verifier types produce structured results:

* **Pass/fail** — Did the agent complete the task successfully?
* **Reward signal** — A numeric score (typically 0.0 to 1.0) indicating quality of completion.
* **Metadata** — Verifier-specific details (which checks passed, which failed, and why).