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

[cerebrixos]

That framing makes sense to me. For a first Prefect example, we would aim for operational usage visibility rather than billing-grade accuracy.

The useful Prefect pattern is: every model call emits normalized request metadata tied to the Prefect flow/task run, plus optional user, feature, workflow, model, provider, token counts, latency, and request IDs. That gives teams enough to answer questions like “which workflow/user/feature is driving usage?” without turning the example into a full billing system.

Billing-grade accuracy probably belongs one layer downstream, because provider token accounting, retries, streaming, tool calls, cached tokens, and model-specific pricing rules can vary. I’d keep the Prefect side focused on durable orchestration and attribution, then let a dashboard or cost service apply pricing and reconciliation later.

For OpenAI-compatible Chinese model layers, I think the same split applies: normalize the response shape and usage metadata per request first, preserve raw provider metadata for audit/debugging, and only then present grouped cost views by user, workflow, or model tier.

[cerebrixos]

We would keep the normalized usage event and correlation IDs close to the workflow run, but keep raw provider evidence outside Prefect.
In practice, Prefect would retain the flow/task identity, request ID, run ID, model/provider, token counts, latency, status, and an external evidence reference. The reference can point to the gateway or provider trace containing the specific attempt, response metadata, retry/fallback history, and any incomplete usage evidence.
That gives Prefect enough information to answer “which task failed and where can I inspect it?” without turning the Prefect database into a provider-log store. The external system can then preserve the attempt-level details needed for debugging and later billing reconciliation.
This is also the boundary we use in our own observability design: normalized usage is easy to group by user, workflow, model, or resource, while provider-native evidence remains auditable through request, run, and trace identifiers rather than being flattened into a potentially inaccurate billing record.

[cerebrixos]

We would define a small, provider-neutral contract rather than make the reference completely opaque:
evidence system
request ID
attempt ID or attempt index
provider and model
evidence URI or external ID
completeness status, such as complete, partial, or unavailable
The Prefect flow/task run ID would remain the orchestration correlation key. For retries or fallbacks, each attempt should produce its own usage event while remaining linked to the same task and flow run.
The URI and evidence-system fields should be enough for Prefect to locate the relevant record, while the gateway remains responsible for provider-native logs, retention, access control, and billing reconciliation. That keeps the first example useful for debugging without making Prefect understand every provider’s response format.