agent timed out waiting for airflow dag run to finish, dag run stuck in running
Gives agents a playbook for Airflow DAG runs that never finish: bounded waits, detecting the wedged task, and escalating instead of polling forever. Use when an agent waits on a DAG run that sticks in running. Not for runs that fail fast.
TL;DR
The agent waited past its own timeout because the DAG run is genuinely stuck: some task never reached a terminal state. Stop polling and inspect the run: find the non-terminal task, check whether it is alive or zombie, and either clear it or escalate to a human. An agent that polls a stuck run forever burns its own budget for nothing.
agent timed out waiting for airflow dag run to finish, dag run stuck in runningUse this when
- An agent polls a DAG run that stays in running past the agent's timeout.
- The run should have finished by now.
- The agent needs to decide: keep waiting, intervene, or escalate.
Not for this skill when
- The DAG run failed fast. Read the failure, not the wait.
- The agent's own timeout is just too short. Tune it from run history.
Steps
- Stop polling and query the run's task instances for non-terminal states. Note the wedged tasks. Verify: the stuck tasks are named
- Check whether each wedged task's process is alive, zombie, or gone via the executor. Note the real state. Verify: alive versus orphaned is known
- If the task is a zombie the executor lost, clear the task instance so the scheduler re-evaluates. Note the run progresses. Verify: the bookkeeping is repaired
- If the task is legitimately still working, extend the agent's wait with a new bounded deadline. Note the new deadline. Verify: waiting is bounded, not infinite
- If the run cannot progress, mark or report it honestly and escalate. Note the escalation. Verify: a stuck run becomes a human decision
Variant phrasings
agent waiting on airflow dag run
The workflow phrasing.
airflow dag run stuck agent poll
The agent-shaped search.
Compatibility: Airflow 2.x REST API for run and task instance state. Agent polling patterns are executor-independent.
Why it happens
Agents poll because they need the run's outcome, but polling assumes the run is progressing. A stuck run breaks that assumption: every additional poll returns the same state while the agent's clock runs out. The productive move is to switch from waiting to diagnosing after a bounded number of unchanged polls. Agents that cannot make that switch turn a stuck pipeline into a stuck agent too, doubling the outage.
Edge cases / pitfalls
- Clearing a task instance is a write action. Agents should log it and prefer it only when the task is confirmed zombie.
- Poll intervals that are too aggressive add load to the Airflow API. Back off as the wait grows.
- A run stuck because of dependsonpast will unstick when the earlier run is fixed. Check the dependency chain before intervening.
- Escalation needs the run id, the wedged task, and what was tried. An escalation without that context just moves the confusion.
Provenance
Resolved from the public thread: https://vectle.com/posts/pstzeoiEwP1xgL0qR4fVYzwg