This article provides a complete, practical walkthrough of the Stopanderror Wait Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Automated Airflow DAG Execution with n8n: A Robust Workflow for Task Orchestration and Monitoring
   
   Meta Description:
   Learn how to automate and monitor Apache Airflow DAG runs using n8n's no-code workflow builder. This guide explains a custom workflow that triggers DAGs, waits, checks their status, and handles timeouts or failures dynamically.
   
   Keywords:
   n8n, Apache Airflow, DAG run, workflow automation, Airflow API, task orchestration, workflow monitoring, HTTP request, DAG status, XCom, automation timeout, DevOps tools
   
   Third-party APIs Used:
   - Apache Airflow REST API (v1)
   
   Article:
   
   Automating Apache Airflow DAG Runs with n8n: A Step-by-Step Workflow
   
   Apache Airflow is an immensely powerful tool for orchestrating complex pipelines, commonly used in data engineering and analytics workflows. While Airflow is designed for flexibility and scalability, triggering and monitoring DAG (Directed Acyclic Graph) runs externally can be tedious without the right automation. This is where n8n, an open-source workflow automation platform, comes into play.
   
   In this article, we’ll break down a custom n8n workflow that does more than just launch an Airflow DAG. It intelligently monitors the DAG’s status, waits for completion, and handles failure states or timeouts efficiently — giving you a hands-free, resilient orchestration mechanism.
   
   Overview: What This Workflow Does
   
   This n8n workflow automates the following sequence:
   
   1. Accepts input parameters for the Airflow DAG (dag_id, task_id, conf, wait time, etc.).
   2. Triggers the specified Airflow DAG via HTTP POST.
   3. Checks if the DAG has entered the "queued" state.
   4. Waits and continuously polls the DAG status.
   5. Tracks retry count and waits again if the DAG is still running or queued.
   6. Retrieves task output via XCom if the DAG run is successful.
   7. Exits with error if the DAG fails or takes too long to execute.
   
   Let’s dive into the primary components of the workflow.
   
   1. Input Collection and API Setup
   
   The workflow begins with the “in data” node, which collects key parameters:
   - dag_id: ID of the DAG to run
   - task_id: ID of the task whose output we want
   - conf: Configuration object passed to the DAG
   - wait: Wait interval between polls
   - wait_time: Maximum number of polling attempts
   
   The “airflow-api” node sets the Airflow instance’s base URL — in this case, https://airflow.example.com — and is used later in dynamically building the request URLs.
   
   2. Triggering the DAG Run
   
   The node “Airflow: dag_run” sends an HTTP POST request to:
     
     /api/v1/dags/{dag_id}/dagRuns
   
   This triggers a new run of the specified DAG in Airflow. Authentication is handled using basic HTTP credentials referenced from stored n8n credentials named “Airflow”. The DAG run is initiated with the user-defined JSON config via the conf key.
   
   3. State Monitoring – Is the DAG Queued?
   
   The result of the run is checked immediately in the “if state == queued” node. If the DAG's initial state is “queued”, the workflow proceeds to a “Wait” node that delays execution by a defined number of seconds.
   
   4. Polling for DAG Status
   
   After the wait period, the node “Airflow: dag_run - state” checks the latest DAG state by hitting:
   
     /api/v1/dags/{dag_id}/dagRuns/{dag_run_id}
   
   Depending on the response (queued, running, success, or failed), control branches via a “Switch: state” node to handle each possibility accordingly:
   
   - success → retrieve XCom result
   - queued/running → loop back and poll again, after increasing count
   - failed → error/stop
   - unknown → optional fallback handling
   
   5. Counting Polling Attempts and Enforcing Timeout
   
   To prevent infinite polling, the “count” node tracks how many attempts have been made. The “If count > wait_time” condition checks whether the count exceeds the allowed maximum. If it does, an error is triggered via the “dag run wait too long” node.
   
   6. Handle Final Results or Failures
   
   On a successful run, the XCom return value of the specified task is retrieved using:
   
     /api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/xcomEntries/return_value
   
   This is handled by the “Airflow: dag_run - get result” node.
   
   In case of failure at any stage (e.g., the DAG fails or takes too long), the workflow halts and emits an appropriate error message using the “dag run fail” or “dag run wait too long” nodes.
   
   Benefits of This Workflow
   
   - No-Code Yet Expandable: Built entirely with n8n’s visual automation interface, this solution requires minimal manual coding.
   - Configurable and Reusable: Parameters like wait time, config, and DAG ID allow for high reusability across different pipelines and use cases.
   - Resilient and Fail-Safe: Incorporates timeout and fail checks to prevent stuck executions or silent failures.
   - Feedback via XCom: Fetches output from Airflow tasks via XCom, enabling downstream logic or alerting based on results.
   
   Final Thoughts
   
   When workflow automation meets orchestration tools like Apache Airflow, operations and engineering teams can unlock significant productivity and reliability gains. By combining n8n's low-code flexibility with Airflow's pipeline execution capabilities, this workflow offers a robust and scalable way to manage DAG execution intelligently.
   
   Whether you're triggering data pipelines, ML jobs, or ETL processes, this n8n + Airflow integration is an indispensable template for your DevOps toolkit.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.