What you can build¶

Each page starts from a job a data team already has, shows the Dag that does it, and names what Airflow adds over a script: a schedule, a retryable task per item, an approval gate, or a fan-out sized at runtime. Every Dag ships with the provider and runs against your connections.

Start with Triage support tickets. It needs nothing but an LLM connection and shows the shape most of the others build on: structured output plus dynamic task mapping.

Every Dag needs the provider installed with the extra for your model vendor and a pydanticai connection named pydanticai_default; Quick start covers both. Each page’s “Run it” lists only what that Dag adds.

Scenario	What the model does	What Airflow does
Triage support tickets	Reads each ticket and returns priority, category, summary and next action as a typed record	One retryable task per ticket, results in XCom, one argument away from a schedule
Route pipeline failures to a fix or a person	Picks rerun, page or ignore from the error text, with a confidence score	Runs only the chosen branch, sends low-confidence picks to a human
Block a load when the schema drifts	Compares source and target schemas and reports what would break a load	Branches to the load or to a notification, no migration ever runs
Explain a revenue anomaly	Queries the warehouse and does the pivots in a sandbox to explain a revenue move	Injects the date, holds the credential, destroys the sandbox when the run ends
Monthly report from a survey CSV	Turns a fixed question into SQL over a CSV	Downloads the file monthly, records schema changes before generating SQL, emails the result
Compare companies’ 10-K filings	Splits a comparison question per company and writes the report	Weekly indexing Dag, on-demand analysis Dag, per-company fan-out, review at both ends
Ask questions over a growing PDF corpus	Answers a question from retrieved excerpts, citing them	Weekly indexing Dag, query Dag triggered with a `question` param
Weekly status report with a hallucination check	Assesses progress per proposal, then checks its own report against the evidence	Mapped evidence gathering, deterministic correction step, human review
Classify reviews in bulk at half the price	Labels sentiment for every review in one batch job	Waits up to 24 hours without holding a worker, lands results on object storage
Research agent with human review	Decides which tools to call to answer a research question	Human edits the question first, separate formatting step, approval before delivery

More ideas¶

The same operators cover many other jobs. These do not have an example Dag yet, but each can be built from the patterns shown on the pages above.

Explain a failure in the alert. A task with trigger_rule="one_failed" reads the failed task’s log, @task.llm returns a Literal root cause and a two-sentence explanation, and the notifier posts that instead of a stack trace.
Data-quality triage. Feed null rates, freshness and duplicate counts from your checks task to @task.llm with a Finding list as output_type, then LLMBranchOperator to page, file a ticket, or ignore.
Daily incident digest. Fetch alerts for the data interval, one @task.llm summary per service with .expand(), one synthesis call, ApprovalOperator before it posts.
Release notes from merged pull requests. Weekly HttpOperator fetch, @task.llm_batch to classify each PR at batch prices, one call to draft the notes, a HITLEntryOperator for the editor’s pass.
Invoices into a table. ObjectStoragePath lists new files, @task.llm_file_analysis extracts a typed row from each with .expand(), a SQL operator inserts them, and the Dag emits an Asset so downstream reporting runs when the rows land.
Rewrite a failing query. On a SQL task’s failure, hand the query and the database error to @task.llm_sql with the schema context, and put the rewrite in front of a reviewer before it runs.
Tag and route incoming files. @task.llm_file_analysis on each new object decides its type and sensitivity, @task.branch moves it to the right bucket.
Catalog descriptions. Nightly, for every table that changed, @task.llm writes a column-level description from the schema and a sample, and a task upserts it into the catalog.

Example Dags lists the same Dags by operator.

Note

Dags with HITLEntryOperator or ApprovalOperator pause under airflow dags test until someone answers from Required Actions in the UI of an api-server on the same metadata database. airflow standalone gives you one.