Yeah, when we release that provider officially, Pavan & I might write a blog post on the Airflow site announcing it and the roadmap -- hoping to get some early adopter quotes / feedback too.
On Tue, 10 Mar 2026 at 19:53, Pavankumar Gopidesu <[email protected]> wrote: > As of now not yet published any article. but we have curated a set of > examples here [1]. Article will be a good one :) > > [1]: > > https://github.com/apache/airflow/tree/main/providers/common/ai/src/airflow/providers/common/ai/example_dags > > On Tue, Mar 10, 2026 at 7:42 PM Jens Scheffler <[email protected]> > wrote: > > > Very cool! > > > > you presented some examples during monthly townhall, can we assume these > > examples are contained in source tree? Will there be a Medium article or > > so published with examples? > > > > On 10.03.26 09:25, Amogh Desai wrote: > > > This is really cool, thanks for sharing Kaxil and Pavan. > > > > > > Thanks & Regards, > > > Amogh Desai > > > > > > > > > On Thu, Mar 5, 2026 at 6:34 PM Kaxil Naik <[email protected]> wrote: > > > > > >> Hi everyone, > > >> > > >> Pavan and I have been working on AIP-99 native agentic AI for Airflow > 3. > > >> The first set of PRs have landed. > > >> > > >> The core idea: Airflow already has 350+ provider hooks, each > > >> pre-authenticated through connections. AIP-99 turns those hooks > directly > > >> into AI agent tools. > > >> > > >> What's available now: > > >> > > >> 1. HookToolset: wraps any Airflow hook into AI-callable tools with > > >> explicit allowed_methods: > > >> > > >> from airflow.providers.common.ai.toolsets import HookToolset > > >> > > >> HookToolset(hook=S3Hook(aws_conn_id="my_aws"), > > >> allowed_methods=["list_keys"]) > > >> > > >> 2. SQLToolset: 4 curated database tools (list tables, describe schema, > > >> execute query, fetch results) scoped to specific tables. > > >> > > >> 3. DataFusionToolset — lets AI agents query files on object stores > (S3, > > >> local filesystem, Iceberg) through Apache DataFusion. Agents get > SQL > > >> access to Parquet, CSV, and Avro files without loading them into a > > >> database. > > >> > > >> 4. MCPToolset: connects to external MCP servers via Airflow > connections. > > >> > > >> 5. Task decorators (Operators are also available :) ): > > >> - @task.llm : single LLM call with structured output > > >> - @task.agent : multi-step agent with tool access > > >> - @task.llm_sql : text-to-SQL pipelines > > >> - @task.llm_schema_compare : cross-database schema diffing > > >> > > >> LLM connections are configured through > > >> Airflow's standard connection model, supporting OpenAI, Anthropic, > > Google, > > >> Ollama, etc. > > >> > > >> HITL (Human-in-the-Loop) integration is also in progress as a draft > PR. > > >> > > >> Project Board: > > >> - https://github.com/orgs/apache/projects/586 > > >> > > >> Summit talk where we previewed this: > > >> https://www.youtube.com/watch?v=XSAzSDVUi2o > > >> > > >> Separate from the AI work, AIP-99 also adds an AnalyticsOperator > powered > > >> by Apache DataFusion for high-performance SQL on object stores: > > >> > > >> - AnalyticsOperator — run SQL queries directly against S3, GCS, local > > >> files, and Iceberg tables. Supports Parquet, CSV, Avro. > > >> - @task.analytics decorator — TaskFlow API support for the above. > > >> - Iceberg support via PyIceberg with Glue catalog integration. > > >> > > >> Pavan and I would love it if folks can start testing out and create > > GitHub > > >> issues if you run into bugs. Our intention is to keep it at 0.x > version > > so > > >> we can iterate on it faster. Looking forward to feedback. > > >> > > >> Thanks, > > >> Kaxil > > >> > > > > --------------------------------------------------------------------- > > To unsubscribe, e-mail: [email protected] > > For additional commands, e-mail: [email protected] > > > > >
