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

> ## Agent Instructions
> To act on Atlan objects, use the Atlan MCP server at https://api.atlan.com/mcp or the SDK. To install or publish skills from a terminal, use the `aireg` CLI; `aireg --map json` prints its command map. The `atlanai` CLI is deprecated. Run a read-only identity check before any write.
> The docs MCP server at /mcp searches these docs only. It cannot read or change Atlan objects.
> SDK packages: Python `atlanai` (PyPI) and TypeScript `@atlanai/sdk` (npm). Show Python first, then TypeScript.
> These docs cover Atlan AI Registry, Atlan's hosted product. The AI registry specification and aireg CLI are maintained separately. /open-source lists publicly reachable signed installers and explains that the source repository and hosted CLI reference currently require GitHub access. Do not claim the repository or its reference docs are publicly accessible. Use these docs for Atlan-specific setup such as the adapter, sign-in and workspaces.

# Review experiment results

> Compare experiment cases with a baseline, inspect the evidence behind a score, and read recorded context changes.

Open an experiment to inspect its individual cases and compare it with a completed baseline. You need access to the experiment and its workspace.

## Open the run

1. Open **Evals → Experiments** and select the agent or other subject, then the run. Runs without a subject can also open from the experiment list.
2. Check its status and recorded case count before reading score averages.
3. Review the subject, dataset snapshot, scorers, baseline, and recorded model or configuration.

An experiment marked `completed` means its runner finished and finalized it. Inspect case errors and score coverage before treating that status as a quality result.

## Compare cases

1. Open **Cases**.
2. Choose a baseline with **Change**, or select **No baseline** when no comparison applies.
3. Search, filter, and sort the cases to find regressions, improvements, or failures.
4. Select a case and compare its input, output, expected result, scores, and available trace evidence.

Compare like cases under the same criteria. An aggregate change can come from a changed dataset, scorer, model, or context; the run's recorded configuration helps you check those differences.

Use **Set as baseline** to make a completed run the default comparison for that subject on this device. This preference applies to the subject page, run page, and new-experiment drawer on the same device. It does not set an organization-wide baseline or change an already stored experiment's baseline.

## Inspect what changed

Open **Diff** to compare the recorded manifest with the baseline. Changed items appear first. When both runs pin a skill version, the view can show the `SKILL.md` changes between those versions.

**Not pinned** means a version was not recorded. **This run pinned no manifest** means there is no stored context manifest to compare. A recorded manifest identifies what the runner reported; it does not prove that a local harness installed those versions. The [desktop runner](/evals/how-tos/run-desktop-experiment#current-limits) uses its installed context.

If a skill's package changed but `SKILL.md` is identical, inspect the supporting files in the skill's [Source tab](/registry/skills/concepts/source).

## Decide the next change

* Reproduce a regression on the same cases before changing the baseline.
* Keep a failed production case in the dataset so the next run checks it again.
* Use **Rerun** to open a new experiment setup, then verify its local runner and configuration.
* Use [CI gates](/evals/ci) when the comparison must be an automated pull request check.

For reproducible comparisons from code, see [Compare two agent versions](/evals/cookbooks/compare-versions).


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.