Airflow-native governed AI endpoint example?

[cerebrixos]

I had opened a small DAG how-to PR for calling Tuning Engines from Airflow, but I want to make sure any follow-up is useful to Airflow users rather than a generic gateway listing.

Would an Airflow-native example be a better fit if it demonstrates:

• storing the inference key in an Airflow Connection or Secret Backend
• using Airflow retries/timeouts/pools to control LLM call reliability and spend
• passing run_id and request_id from an Airflow task into the governed inference request
• returning only safe summaries/metadata through XCom
• linking Airflow task logs to Tuning Engines traces, policy decisions, approvals, token usage, and cost

The pattern would keep Airflow as the scheduler/orchestrator and Tuning Engines as an OpenAI-compatible governed inference endpoint. I can open a replacement PR if a cookbook/how-to like that fits the docs, or keep it external if the project prefers not to include third-party endpoint examples.