Qualify AI-generated code before pipeline release
Qualify AI-generated code for Polyaxon pipelines using immutable candidate identity, independent evaluation, explicit approval, and versioned release artifacts.
A generated script that worked once in a sandbox is not yet a pipeline component. Production code needs repeatable dependencies, defined inputs, failure behavior, and evidence tied to the version being released.
Use Polyaxon to turn exploration into a qualification workflow. Keep the candidate, evaluator, and promotion step distinct so a useful experiment can become a maintainable platform asset.
Freeze the candidate before evaluation
Collect the proposed source change as a patch or commit against a known base. Record a digest and the generation run UUID. Do not evaluate a moving workspace while the agent continues editing it.
For a feature-processing script, also preserve the input schema, expected output contract, and dependency lockfile. If the code downloaded packages interactively, move those requirements into a reviewed image before qualification.
Use sandbox output persistence to retrieve useful files, then store the accepted candidate through the artifact or source-control workflow.
Build an independent qualification profile
Choose checks based on the component's intended role:
| Component | Qualification examples |
|---|---|
| Data transformation | Schema, missing values, determinism, row reconciliation |
| Model evaluator | Metric correctness, fixture coverage, failure reporting |
| Agent tool | Authorization, malformed inputs, deadlines, output bounds |
| Deployment helper | Dry-run behavior, approval path, idempotency |
The evaluator implementation and fixtures should be reviewed independently. Generated code should not be able to rewrite the expectations used to approve it.
Run the qualification as a finite Polyaxon job using a known image and explicitly supplied inputs. Dependency ordering alone does not transfer files between stages; configure the candidate artifact as an input.
Make rejection observable
Separate infrastructure failure from qualification failure. An unavailable dataset should not be reported as “candidate passed with no errors,” and a failed quality threshold should not be hidden inside an otherwise successful report.
Record numerical results with tracking metrics and detailed findings as artifacts. Have the trusted evaluator return a clear outcome and, when appropriate for the workflow, exit unsuccessfully if mandatory checks fail.
Logging a low score does not automatically block downstream release. The workflow must explicitly consume the evaluator's decision.
Promote the exact reviewed version
Before approval, verify that the source or patch digest matches the evaluated candidate. Reevaluate after material changes, rebases, or dependency updates.
Package the approved implementation as a versioned component. Record its image revision, input contract, evaluator revision, and approval reference.
Keep publication or deployment credentials out of the exploratory sandbox. A trusted release step should perform promotion only after the configured approval process.
Keep a rollback and maintenance path
A qualified component still needs monitoring when real inputs change. Preserve representative failure cases and add them to future qualification runs.
Use run comparisons to compare new candidates with the currently accepted version. Track resource use and runtime as well as correctness; a functionally correct change may still be unsuitable if it doubles the workload's cost.
The result is a clear handoff from agent experimentation to platform engineering: generated proposals remain easy to explore, while released components carry reproducible evidence and an explicit owner.