# Agentic Workloads on Airflow: Observable, Retryable, and Auditable by Design

DevFeed: [Agentic Workloads on Airflow: Observable, Retryable, and Auditable by Design](<https://devfeed.tech/articles/agentic-workloads-on-airflow-observable-retryable-and-auditable-by-design-32526.md>)

Original publisher: [Read original article](<https://airflow.apache.org/blog/agentic-workloads-airflow-3/>)

Author: Apache Airflow

Published: 2026-04-15T00:00:00Z

Content type: tutorial

Language: en

Sources: [Apache Airflow Blog](<https://devfeed.tech/sources/apache-airflow-blog.md>)

Topics: [airflow](<https://devfeed.tech/topics/airflow.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [SQL](<https://devfeed.tech/topics/sql.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Agent Harness](<https://devfeed.tech/topics/agent-harness.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [ai](<https://devfeed.tech/tags/ai.md>), [airflow](<https://devfeed.tech/tags/airflow.md>), [community](<https://devfeed.tech/tags/community.md>), [csv](<https://devfeed.tech/tags/csv.md>), [llm](<https://devfeed.tech/tags/llm.md>), [sql](<https://devfeed.tech/tags/sql.md>), [survey](<https://devfeed.tech/tags/survey.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

## AI overview

This article explains how to build agentic workloads on Airflow 3 using the 2025 Airflow Community Survey dataset and the apache-airflow-providers-common-ai provider. It distinguishes single-query LLM pipelines from workflows that query multiple independent dimensions and synthesize their results, mapping each sub-query to a named Airflow task and using Dynamic Task Mapping and XCom for orchestration and observability.

## Source excerpt

A question like "How does AI tool usage vary across Airflow versions?" has a natural SQL shape: one cross-tabulation, one result. A question like "What does a typical Airflow deployment look like for practitioners who are actively using AI in their workflow?" does not. It requires querying executor type, deployment method, cloud provider, and Airflow version independently, each filtered to the same respondent group, then synthesizing the results into a coherent picture. No single query returns the answer. The answer emerges from the relationship between all of them. This is where Airflow's agentic pattern begins: not when you add an LLM to a workflow, but when the structure of the work itself depends on running multiple LLM calls whose outputs feed a synthesis step. This post builds that pattern using the 2025 Airflow Community Survey data set and the apache-airflow-providers-common-ai provider for Airflow 3. If you haven't read the introductory survey analysis post yet, start there for a walkthrough of the single-query interactive and scheduled pipelines. This post picks up where that one ends. The Agentic Gap in the Single-Query Pattern The interactive and scheduled survey DAGs from the introductory post each do one thing: translate a natural language question into SQL, execute it against the CSV, and return the result. The LLM is involved once. The structure of the pipeline does not change based on what that LLM call returns. That is not a limitation to fix. It is the right design for that class of question. For a large fraction of production AI workflows, a single well-structured LLM call with good context is sufficient and preferable. The pattern becomes agentic when two things are true simultaneously: The question requires querying multiple independent dimensions The synthesis step, the thing that produces the final answer, depends on all of those results In an agent harness framework, this would be handled inside a reasoning loop: the LLM decides to call a to