Automation workflow testing

When to use this

• You are wiring the model into a scheduled job, queue worker, or pipeline.
• You need to validate behaviour when retries, timeouts, and downstream parsers come into play.
• You want a regression-aware test plan that catches silent quality drops.

Test setup

• Map the automation pipeline end to end — input source, model step, downstream parser, output destination.
• Decide which steps are idempotent and which are not — non-idempotent steps need stricter guards.
• Pick a representative shadow run set and a small canary set you can run repeatedly.
• Pin a representative workload size (jobs per hour, prompts per job) so cost projections stay honest.

Minimum test set

• 20–50 shadow jobs that mirror real production input distribution.
• 5–10 deliberate adversarial jobs (corrupt input, partial input, hostile input).
• A small canary set you re-run after every snapshot rotation to catch regressions.

Prompt variants

Vary one dimension at a time so you can attribute behaviour.

• Same job, two candidate models, to surface candidate-specific failure modes.
• Same job, two snapshots of the same model, to surface version drift.
• Same job, with and without retries, to confirm retries do not amplify errors.

Observations to record

Record observations, not scores. The evidence brief stays auditable.

• Pass / fail per shadow job against your acceptance rubric.
• Retry rate, retry success rate, and final failure rate.
• Downstream parser error rate — sometimes the model is fine and the parser is wrong.
• End-to-end latency including retry overhead.
• Per-job cost projection rolled up to your expected daily volume.

Failure modes to watch

• The model passes single-shot prompts but the retry loop amplifies a malformed output.
• Downstream parser tolerates one model's quirks and breaks on another — selection evidence.
• Job succeeds on the canary set but degrades on shadow traffic — production distribution mismatch.
• Latency is fine median but tail latency exceeds your queue timeout.

Stop conditions

Knowing when to stop is part of the test.

• Stop and re-scope if retry rate exceeds your acceptance ceiling during shadow runs.
• Stop and document if the same job passes the canary set and fails the shadow set — investigate input distribution drift.

Outputs

• A Markdown evidence brief covering shadow-run pass rate, retry behaviour, and tail latency.
• An automation runbook section listing the canary set and the regression schedule.

Weak test vs stronger test

Observation rubric

Observations, not scores. Each row names what to look at and what to record — there is no aggregate number.

Record this in your brief

Brief content that keeps the playbook output reusable for a reviewer.

• Embed the shadow-run protocol so the brief is reproducible.
• Pair shadow-run acceptance with retry behaviour — they interact.
• Record tail latency, not just median.
• Document the canary suite and regression cadence.