ori-cofounder opened a new issue, #63679: URL: https://github.com/apache/airflow/issues/63679
## Proposal: GNAP as the handoff layer between Airflow DAGs and autonomous AI agent tasks Apache Airflow is the standard for workflow orchestration — DAGs, schedules, operators, and sensors have powered data pipelines for years. As AI agents become participants in those pipelines, there's a gap: how does an Airflow task hand work off to an autonomous agent and wait for completion? [GNAP](https://github.com/farol-team/gnap) (Git-Native Agent Protocol) fills this gap cleanly. A git repo serves as the coordination board: Airflow tasks write to `board/todo/`, agents claim to `board/doing/`, and commit results to `board/done/`. Airflow's existing file sensor or a custom GNAP sensor can watch for completion. **Applied to Airflow's DAG model:** ```python # Airflow DAG create_gnap_task = PythonOperator( task_id='create_gnap_task', python_callable=write_to_gnap_board, op_args=['board/todo/analyze-dataset-{{ ds }}.md', task_spec] ) wait_for_agent = GNAPSensor( # custom sensor task_id='wait_for_analysis', gnap_path='board/done/analyze-dataset-{{ ds }}.md', timeout=3600, poke_interval=30 ) create_gnap_task >> wait_for_agent >> downstream_processing ``` The autonomous agent (LangChain, AutoGPT, custom) picks up the task, processes it, and commits to `board/done/`. Airflow resumes the DAG. This pattern is particularly valuable for Airflow users adopting AI incrementally — you keep existing DAG infrastructure while adding autonomous AI steps without new message brokers. Spec: https://github.com/farol-team/gnap -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
