You won't believe, I was thinking about something like this just last week.
So happy to see the community also moving in the same direction


On Sat, Apr 4, 2026 at 6:57 PM Aritra Basu <[email protected]> wrote:

> Overall a big +1 to this, I've added a few comments on it. But good read,
> would love to see this implemented and always open to helping get it to the
> finish line! Thanks for the proposal!
> Thanks and Regards,
> Aritra Basu
>
>
> On Fri, Apr 3, 2026 at 6:33 PM Shahar Epstein <[email protected]> wrote:
>
> > Hello everyone,
> >
> > Since I first opened the thread discussing the MCP server, I've been
> > thinking for a long while about how we can practically bring AI-assisted
> > debugging and operational insights directly into Airflow. As
> orchestration
> > environments grow more complex, the cost of troubleshooting, such as
> > navigating across Dag code, task instances, scheduler logs, and
> > configurations, translates directly to lost on-call time and delayed
> > pipelines.
> >
> > Today, organizations that want AI-assisted debugging are forced to build
> > custom, ad-hoc integrations or rely on external paid solutions. This
> leads
> > to fragmented user experiences, duplicated effort, and most critically,
> > inconsistent security controls that risk exposing sensitive metadata or
> > bypassing Airflow's native Role-Based Access Control (RBAC).
> >
> > I think we can do better, by proposing AIP-101: Airflow AI Assistant -
> > Phase 1 (Read-only assistance). This AIP introduces an official, opt-in
> > plugin that provides a conversational UI directly within Airflow to
> answer
> > user questions about their instances, explain errors, and help
> troubleshoot
> > failures.
> >
> > To ensure this is done safely and securely, Phase 1 is strictly
> read-only.
> > The assistant does not modify Airflow state, nor does it operate
> > autonomously. Instead, it relies on the newly proposed Airflow MCP Server
> > (AIP-91) as its data-retrieval engine. By leveraging the MCP standard,
> the
> > assistant guarantees that its answers are grounded in live system state
> > while strictly enforcing the authenticated user's RBAC permissions so the
> > AI never accesses data the user cannot see.
> >
> > tl;dr of the proposed implementation:
> >
> > Packaging: Delivered as an opt-in, standalone plugin package within the
> > apache/airflow monorepo (with an independent release cycle).
> > Frontend: A conversational UI embedded directly in the Airflow web
> > interface.
> > Backend: A FastAPI-based plugin backend utilizing pydantic-ai to safely
> > orchestrate external LLM calls.
> > Data Access: Relies entirely on the Airflow MCP Server (AIP-91) to fetch
> > read-only state.
> >
> > ---
> >
> > Because the assistant is heavily coupled with the secure tool-calling
> > execution provided by the MCP server, which is covered in a separate AIP
> > (AIP-91) - please note the ongoing discussion here, as well as AIP-91
> > itself: https://lists.apache.org/thread/xgd66v6s7zf0xkvy3c7ysqvn4csgmw06
> > https://cwiki.apache.org/confluence/x/G4q3FQ
> >
> > ---
> >
> > AIP-101 is available here:
> > https://cwiki.apache.org/confluence/x/8Ic8G
> >
> > A quick warning before you read: the AIP is quite long! (sorry Jarek)
> > Because integrating AI into an orchestrator opens up a lot of potential
> > pitfalls, [ChatGPT and] I tried to be extremely thorough in covering all
> > the possible stuff that could go wrong :)
> > If you find a specific section to be overly detailed or repetitive,
> please
> > comment in the AIP and I'll try to handle it.
> >
> > I've managed to build a very inital POC, screenshots are available in
> this
> > section:
> >
> >
> https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=406620144#AIP101AirflowAIAssistantPhase1(Readonlyassistance)-BehavioralModel
> >
> > I would love to hear your thoughts. Please comment on the AIP and/or
> reply
> > to this thread.
> >
> > Thank you,
> >
> > Shahar
> >
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> >
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