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

# Run a simulation

> Kick one off from the app or CLI — pick the target, pick the dataset, go.

Simulations are cheap to start and fast to iterate. The workflow is intentionally lightweight.

## From the app

1. **Reliable → Simulation → New run**.
2. Pick a **target**:
   * Current Draft.
   * A specific published version.
   * An Improvement candidate.
3. Pick a **dataset** (or dataset subset via tag filter).
4. Optionally: pick a **baseline** to compare against (defaults to current published).
5. **Preview** — shows the number of examples, estimated cost, estimated time.
6. **Run**.

Progress streams in real time. You can leave the page and come back — completion notification lands in your workspace notification bar.

## From the CLI

```bash theme={null}
nora simulate \
  --flow support-agent \
  --target draft \
  --dataset support-golden-v3 \
  --baseline published
```

Same options: `--target` accepts `draft`, `published`, `version:<id>`, or `improvement:<id>`. `--baseline` accepts the same or `none`.

Add `--tags customer-facing,hard` to filter the dataset.

Add `--parallelism 20` to control concurrency (default 10).

## From CI

Simulations block-or-pass a deploy pipeline. Standard pattern:

```bash theme={null}
nora flows publish --dry-run --gate-simulation support-golden-v3 --min-pass-rate 0.9
```

The `--dry-run --gate-simulation` combo runs the target Draft against the dataset. If pass rate is under the min, the command fails and CI stops.

Common: run gate simulation on every PR, block merge if it fails.

## What runs where

Every simulation runs in an isolated environment:

* Tools that require credentials use **test credentials** (a separate connector per Flow, marked test-only). Prevents accidental production side-effects during a run.
* Actions marked "must not run in simulation" are stubbed. Their outputs are simulated to `success`.
* Memory writes go to a per-simulation scratch space that's discarded after.

You can override the environment (**Run → Advanced → Environment**) to run against production credentials — rare, only when you specifically want end-to-end validation. Add safeguards.

## Watching results

The results table shows one row per example:

* **Input** — the dataset example's input.
* **Target output** — what the target produced.
* **Baseline output** — what the baseline produced (if comparing).
* **Expected** — the dataset's expected output.
* **Verdict** — pass / fail / regression / improvement.
* **Cost** and **latency**.

Sort, filter, click any row for the full trace of that example.

## Stopping

Long simulations can be stopped at any point. Partial results are kept and marked "partial". Useful when it's clear the change isn't working after the first 20 examples.

## Re-running

Re-running the same simulation is possible but usually pointless — same target + same dataset = same result (with model randomness noise). What you probably want:

* **Re-run with new dataset version** — pick up new examples.
* **Re-run with new target** — after adjusting your Draft based on this run's insights.

Every re-run creates a new simulation record, so history is preserved.