Introducing AI-Assisted Migrations with Otto: Your Airflow Expert for the Translation Work that Stalls Every Project

Every AI initiative your organization is running depends on data pipelines that are reliable, current, and governed. That dependency has turned orchestration modernization from a deferred infrastructure project into an active blocker. Tools like Control-M and AutoSys were built for a different era of data operations. They have no path to the event-driven workflows, dynamic branching, or agent orchestration that production AI requires. For most data teams, the migration is no longer a someday project, it's the thing blocking everything else.

What we've seen across hundreds of migrations is that projects don't usually stall because Airflow is the wrong destination, but because getting there is harder than it looks. Every legacy scheduler has its own job definition format, scheduling semantics, and dependency model. Converting that inventory into idiomatic, production-quality Airflow Dags requires someone who deeply understands both the source system and Airflow best practices at the same time. That dual expertise is rare. So the work gets scoped, the complexity gets underestimated, and the project slips a quarter if not longer. Teams that do push through often end up with converted Dags that work but weren't written by anyone who actually knows Airflow well, and they carry that technical debt forward.

It doesn't have to go that way. We've spent years helping customers work through tedious, complex migrations to Airflow. Today, that expertise is built into Otto, Astronomer’s data engineering agent, and now available for early access.

Why the destination matters

Apache Airflow has become the standard for data orchestration for good reasons: open source, Python-native, 2,100+ integrations, 19 million+ monthly downloads, and production deployments at organizations that run some of the most complex data pipelines in the world. Moving off tools like Control-M, AutoSys, or a legacy enterprise scheduler onto Airflow is the right call. The question is always how to get there cleanly.

There's a second trap worth naming: self-managed Airflow trades one operational burden for another. Teams that migrate onto OSS Airflow often find themselves back in the same position a year later, managing infrastructure instead of building pipelines. The destination worth migrating to is managed Airflow on Astro, where upgrades happen without multi-sprint planning cycles, failures surface with context, and the platform scales without your team babysitting it.

Astronomer has been helping customers accelerate complex migrations from tools like Control-M, AutoSys, and others for years. We know where the complexity hides, what breaks, and how to sequence a migration so it lands cleanly and doesn’t take quarters to complete. That institutional knowledge is exactly what we've built into Otto.

Accelerate migrations with an Airflow expert in agent form

Otto isn't just a translation script. A translation script converts syntax, it takes your Control-M XML or AutoSys JIL and produces Python that runs in Airflow. Sometimes that output is good, more often it's technically functional but not idiomatic, not optimized for your provider versions, and not written the way an experienced Airflow engineer would write it. You end up with converted Dags that need significant rework before they're production-ready.

Otto understands Airflow — it carries Astronomer's proprietary knowledge of Airflow best practices, your target Airflow version and provider stack, and the conventions stored in your team's memory into every migration conversion. The Dags it produces follow patterns that experienced Airflow engineers have refined across years of production deployments. They're built for your environment, not a generic target.

Otto supports migrations from tools like Control-M, AutoSys, Automic (formerly UC4), and other common schedulers, in addition to migrations across Airflow environments (OSS, Amazon MWAA, or Google Managed Service for Apache Airflow). It maps dependencies as part of the translation process, surfacing the hidden complexity that ambushes most migration programs mid-flight before it becomes a production problem. The output is deterministic and traceable: every Dag maps back to its source job definition, so your team can validate correctness before anything reaches production.

Migration as context-layer investment

Most migrations end when the last job is converted. The institutional knowledge accumulated during the project, how this team names Dags, which operators they prefer, what the conventions are for retry policies and connection configs, disappears into a project retrospective that nobody reads.

Otto changes that equation. Every convention your team establishes during migration gets stored in Otto Memory. The patterns that get codified as you work through your job inventory don't evaporate when the engagement ends. They become the context layer that makes Otto more useful for every authoring, upgrade, and investigation task that comes after.

With Otto, you aren’t just migrating your workflows, you are building the foundation for how Otto works with your team going forward.

The full data pipeline lifecycle, covered

Otto was built to handle every part of the operational work that keeps a data team running. Author new pipelines with your conventions already in place. Plan and execute Airflow upgrades without the multi-sprint manual analysis. Diagnose production failures using your deployment's actual operational history. And now, get your existing workflows onto the platform in the first place, without the translation debt that follows most migrations for years.

Every team that uses Otto for migrations seeds their own private memory layer, the conventions, patterns, and institutional knowledge Otto carries into every future session for that organization. It doesn't train Otto for anyone else, it makes Otto work better for you.

Otto for migrations is available in early access.

If you're planning a move off Control-M, AutoSys, or another enterprise scheduler, and are interested in using Otto, we'd love to work with you. We'll scope the complexity and build a plan that fits your timeline.

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Still evaluating whether to migrate?

Our latest whitepaper covers the five critical considerations for migrating off legacy schedulers to modern orchestration.

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