Building AI Debugging Agents Into Airflow DAGs at Jeppesen ForeFlight with Samantha Blaney Cuevas
About this episode
Samantha Blaney Cuevas discusses the integration of AI debugging agents into Airflow DAGs for managing aviation data pipelines at Jeppesen ForeFlight.
Aviation data pipelines run on strict 28-day publication cycles, and the margin for error is zero. In this episode, we're joined by Samantha Blaney Cuevas , Software Engineer at Jeppesen ForeFlight , to explore how her team orchestrates a complex, time-sensitive data pipeline with Airflow and where AI is starting to fit into that picture. Key Takeaways: 00:00 Introduction. 04:05 Airflow orchestrates almost all business logic and data transformations across the cycle, with custom timetables built to track busy and slow periods programmatically. 06:10 Cycle-aware sensing tasks handle irregular source deliveries, including duplicates and early or late arrivals, without disrupting the pipeline. 08:07 The two main AI use cases are pipeline debugging and cycle awareness — both designed to reduce the manual overhead of monitoring a complex DAG dependency graph. 09:03 The Data Port agent is a two-task DAG that routes Slack pipeline alerts to either a predefined command list or an AI token, depending on whether the fix is already known. 13:10 AI is still in development at Jeppesen ForeFlight — the team is focused on token efficiency and scoping how much autonomy to give agents across…
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