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

> ## Agent Instructions
> Install with `uv add whileai`; import as `import whileai as wai`.
> Run the offline path first (`simulator=False`, `wai.seeded_agent`, a callable judge); no key is needed for it.
> Report every pass rate with its interval and n, as `scored.pass_at` prints it.

# Report a run so a person can decide

> What you learn: the typed objects the platform tracks (a tracked agent with its harness, behaviors, runs, live traffic), why a harness is versioned by its fingerprint, and why a version is scored on every behavior.

<Note>The scripts are in the repository, not in the installed package. Clone it,
then `cd recipes/04-train/report-run` before running the commands below. [Browse this recipe on GitHub](https://github.com/whilehq/whileai-sdk/tree/main/recipes/04-train/report-run).</Note>

**What you learn**: the typed objects the platform tracks (a tracked agent
with its harness, behaviors, runs, live traffic), why a harness is versioned
by its fingerprint, and why a version is scored on every behavior.

**Needs**: `WHILEAI_API_KEY` for the real thing; nothing for the smoke run.

**Takes**: 10 seconds.

```bash theme={"theme":"vitesse-dark"}
python run.py                 # posts one full loop for agent refund-bot, prints the verdict
python run.py --agent my-bot  # your own name
python run.py --offline       # no key, no network: prints the calls it would make
```

The script registers an agent (model + harness + the frontier model you pay
for today), declares five behaviors with their own held-out tests, opens
one run per version (base, v1..v4) with a training curve and a score on
every behavior, marks v3 as served, posts two weeks of traffic, and reads
the verdict back. Open withwhile.com/platform/runs afterwards: that is the screen
the person decides on.

Three rules a reported score has to keep \[1, 2]: the held-out
test for a behavior does not change under you (bump `test_version` when it
does; the platform does not yet refuse a score on another test version, so
this one is on you), a score without an interval is not a result (`ci` is
the half-width of the 95% interval, and `verdict()` says "not a result"
without one), and a delta inside the eval's own re-run band is not a result
either (`noise_floor` is the spread you saw scoring the same model twice;
`verdict()` reads it). The `--offline` verdict is computed from the score
table with the same difference-interval rule the platform uses.

## References

1. Lambert, N. Reinforcement Learning from Human Feedback. arXiv:2504.12501, 2025. Chapter *Evaluation*.
2. Miller, E. Adding Error Bars to Evals. arXiv:2411.00640, 2024.