Handing Apache Airflow® work to an agent only pays off if the agent actually understands Airflow, not just Python. This session shows how Otto, Astronomer's data engineering agent, can catch what generic tooling misses and validate its own work before shipping, reasoning from years of Astronomer running Airflow in production at scale. See it applied across upgrades, code review, and everyday pipeline maintenance.
Join us to learn how to:
- Upgrade Airflow from 2.x to 3 with less manual risk-checking, using a compatibility knowledge base that flags what's actually unsafe to change
- Catch Airflow-specific issues, deprecated operators, provider conflicts, convention violations, before they ship, without adding a manual review step
- Put an agent to work on everyday Airflow maintenance while keeping full visibility into what it touches and why
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