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] > >
