Why AI runs need a frozen configuration
If a pipeline changes while a batch is running, every record should still have an explainable execution history. Freezing the configuration at run start gives that history a stable reference.
Separate edits from execution
Imagine changing a support taxonomy halfway through a batch. If later rows read the current pipeline while earlier rows used the previous version, one run contains two policies. It becomes harder to compare results or repeat failures. A frozen configuration makes the edit apply to a later run while the existing batch completes under its original rules.
- 1Record the configuration or revision used when…Record the configuration or revision used when starting work.
- 2Inspect that version when diagnosing a row…Inspect that version when diagnosing a row result.
- 3Compare changed behavior using a new run…Compare changed behavior using a new run on the same fixture.
Try it on a small example
- Record the configuration or revision used when starting work.
- Inspect that version when diagnosing a row result.
- Compare changed behavior using a new run on the same fixture.
What to verify
Baleybots executes a config_snapshot frozen at enqueue time and appends a revision when a pipeline is edited. That stabilizes the graph and processor configuration. It does not guarantee identical output from a nondeterministic model or an external source whose data has changed. Preserve input evidence and relevant model settings alongside the configuration when reproducibility matters.
Product details checked against the Pipeline architecture on October 4, 2026. These guides describe documented behavior; availability depends on your account and the deployed service. Baleybots is in invite-only beta.