This article provides a complete, practical walkthrough of the Elasticsearch Cron Create 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 Alert Handling: Using n8n to Monitor Elasticsearch and Create Azure DevOps Work Items
   
   Meta Description:  
   Learn how to automate daily alert monitoring in Elasticsearch with n8n and create corresponding tasks in Azure DevOps if issues are found. Discover this practical integration using a no-code workflow.
   
   Keywords:  
   n8n workflow automation, Elasticsearch monitoring, Azure DevOps integration, DevOps alert management, no-code automation, create work items from alerts, cron-based workflows, workflow orchestration, automated task creation, Elasticsearch alerting
   
   Third-party APIs Used:
   
   - Elasticsearch  
   - Azure DevOps REST API
   
   Article:
   
   In today’s fast-paced DevOps environments, automation is key to maintaining continuous monitoring and rapid responsiveness. Managing alerts generated from log and data analysis tools like Elasticsearch can be time-consuming when done manually — especially when analysts need to sift through countless logs to determine which require action.
   
   That’s where n8n, a powerful open-source workflow automation tool, comes in. In this article, we break down an n8n workflow that monitors Elasticsearch on a daily basis and, if an alert condition is met, automatically creates a work item in Azure DevOps. This no-code solution saves valuable time, reduces manual errors, and keeps engineering teams focused on resolving important issues rather than chasing down logs.
   
   Workflow Overview
   
   Here’s a summary of what this n8n workflow does:
   
   - It triggers daily at 12:15 PM.  
   - Runs a query on Elasticsearch to detect any critical issues or alerts.  
   - If any alerts are found (based on the number of results), it creates a new task in Azure DevOps.  
   - If no alerts are found, the workflow gracefully ends with no further action.
   
   Let’s break this down step by step.
   
   Step 1: Cron Trigger - Scheduled Execution
   
   The workflow begins with a Cron Trigger node. It’s configured to run once a day at exactly 12:15 PM. This ensures that the Elasticsearch index is checked for issues at a consistent time each day, enabling teams to start triaging new tasks during their workday routine.
   
   Sample Configuration:
   - Hour: 12  
   - Minute: 15
   
   Step 2: Elasticsearch Query - Monitoring for Alerts
   
   Next, we have the Elasticsearch Query node. This node performs a search in an Elasticsearch index. While the specific query isn’t detailed in this workflow JSON, this node is the heart of the monitoring mechanism. It could be searching for error logs, failed transactions, or any other metric deemed critical by the engineering or DevOps team.
   
   Elasticsearch is commonly used in logging stacks such as ELK (Elasticsearch, Logstash, Kibana), making it a prime candidate for alert detection.
   
   The response returns a JSON object, including a count of matched results inside the hits.total.value parameter.
   
   Step 3: IF Node - Conditional Alert Check
   
   After retrieving the search results, the workflow passes the data to an IF node named “Check for Alerts.” This node evaluates whether any alerts were returned by Elasticsearch:
   
   Condition:
   - If hits.total.value > 0, it means there are issues worth investigating.
   
   This condition creates a logical decision tree:
   - True: Alert(s) detected —> proceed to create a work item.  
   - False: No alerts —> continue to a No Operation (NoOp) and exit.
   
   Step 4: HTTP Request to Azure DevOps - Creating Work Items
   
   When alerts are found, the workflow continues to the “Create Work Item” HTTP Request node. This node integrates with the Azure DevOps REST API and specifically targets the work item creation endpoint. It uses a POST request to generate a new task in Azure Boards for the relevant team to track and resolve the issue.
   
   Key Details:
   - API Endpoint: https://dev.azure.com/<organization>/<project>/_apis/wit/workitems/$Task?api-version=7.1-preview.3  
   - Content-Type header: application/json-patch+json  
   - Authentication: Basic Auth (assumes credentials are securely stored in n8n)
   
   The payload could include custom fields like title, description, tags, and assignment, but in this example, the configuration focuses primarily on the connection setup.
   
   Step 5: No Operation - Graceful Exit
   
   If no alerts are found in Elasticsearch, the False path from the IF node routes to a simple No Operation (NoOp) node. This node acts as a placeholder and safely terminates the workflow without taking any additional action — a clean, effective way to prevent any unnecessary processing or logging.
   
   Benefits of This Workflow
   
   This n8n workflow illustrates several important principles in modern DevOps and automation:
   
   - Scheduled Monitoring: Run critical queries on a daily cadence without human intervention.
   - Responsive Alerting: Automatically escalate issues by integrating directly with Azure DevOps.
   - Reduced Manual Effort: Avoid opening dashboards or copying/pasting alerts.
   - Scalable: Can be modified and expanded to include Slack notifications, email alerts, or incident creation in tools like PagerDuty.
   - No-code: Built entirely in n8n with a visual, intuitive interface.
   
   Conclusion
   
   By leveraging n8n’s automation capabilities, teams can simplify their alert pipeline and bridge the gap between observability tools (like Elasticsearch) and task management platforms (like Azure DevOps). This workflow not only helps catch issues early but ensures they're promptly documented for resolution, all without lifting a finger.
   
   Whether you’re an SRE, platform engineer, or DevOps specialist, integrating such automation into your observability strategy is a great way to modernize operations and build a more resilient infrastructure.
   
   Ready to build your own automation? Try n8n today and start integrating your tools with drag-and-drop ease.

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.