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

# Interpreting Results

> Understand Test Run match rates, criteria breakdowns, and comparisons in the Portal.

Once a Test Run completes, its detail page gives you three layers: run-level summary metrics,
per-item scores, and side-by-side comparisons.

## Run summary (macro view)

For **Request** and **Workflow** runs, the summary tiles are:

* **# Items**: total items in the run
* **Completed %**: how many items finished executing
* **Match Rate %**: matched / (matched + mismatched), over items that have expected output
* **Error Rate %**: items that errored during execution

A banner breaks the match rate down into matched / mismatched / without-expected / errored.

For **Agent** runs, the summary instead shows a **Pass Rate**: the average pass rate across the
run's [criteria](/test/test_sets/rubrics), since agent runs are scored by criteria rather than
structural matching.

<Note>
  Scoring runs as a separate pass after execution. Before scoring finishes, item scores show as
  a dash (`-`). You can re-score a completed run after changing its criteria.
</Note>

## Requests table (per-item view)

The items table is where you debug individual cases:

* **Status** for each item execution
* **Score**: a normalized `0–100%` pill; hover it for the **criteria breakdown** (each criterion's
  pass/fail, numeric value, or tag)
* **Actions**: inspect the item, and compare the expected output against the run's actual output

## Criteria breakdown

The per-criterion breakdown adapts to the criterion type:

* **Boolean** → a pass rate, a failure count, and a pass/fail bar
* **Numeric** → a score distribution (a table + bar over the score buckets, colored low→high)
* **Tag** → a categorical distribution across the tag values

Use this to see *which* criterion is driving a low score, then jump into those items.

## Comparing runs

Because runs share the test set as their axis, you can compare them directly:

* **Across runs**: see item-by-item score shifts between two or more runs, including runs against
  *different* consumers (e.g. a model baseline vs. an agent).
* **A single item across runs**: track how one specific scenario changed over time.

Next: [Regression Monitoring](/test/test_sets/regression).