Governed AI workflow example using Prefect and Tuning Engines

[cerebrixos]

Hi Prefect team,

Thanks for the review on #22066. The feedback made sense: the first PR was too much of a generic endpoint example and did not show enough Prefect-specific value.

I wanted to continue the conversation here before opening another PR.

The stronger example I would propose is a Prefect flow for governed AI work where Prefect owns orchestration and Tuning Engines is only the governed model/tool endpoint:

• A Prefect flow runs a multi-step AI task with task retries, timeouts, and result persistence.
• A model-call task uses Tuning Engines' OpenAI-compatible endpoint with TE_INFERENCE_KEY, TE_MODEL, run_id, and request_id.
• A follow-up task records observed-only trace metadata to Tuning Engines so Prefect run history and Tuning Engines policy/usage traces are correlated.
• The example highlights Prefect-specific features such as deployments, retries, task concurrency, parameters, and automations rather than just a simple flow/task wrapper.

The intent would be:

• Prefect owns schedules, retries, deployments, flow state, and operational recovery.
• Tuning Engines owns model routing, policy decisions, approvals, usage attribution, and trace correlation.

Would an example like that fit better in the Prefect examples/docs? If yes, I can open a fresh PR shaped around the Prefect-native parts rather than the generic gateway setup.

[cerebrixos]

Thanks for the direction on making this more Prefect-native. I opened a replacement PR with a fuller example around task retries/timeouts, global concurrency limits, persisted results, deployments, and run/request trace correlation: #22162