This article provides a complete, practical walkthrough of the Jiratool Schedule Automate Scheduled 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:**  
   Supercharge Support with AI: Automatically Triage and Close Long-Lived Jira Issues Using n8n
   
   **Meta Description:**  
   Discover how this powerful n8n automation uses AI to triage, resolve, and close forgotten Jira tickets. Learn how GPT-4o, Notion, and Slack are leveraged to handle support tickets efficiently.
   
   **Keywords:**  
   n8n automation, Jira automation, AI ticket triage, close old Jira issues, GPT-4o, Jira workflow, AI support automation, Notion integration, Slack notifications, customer support automation, Jira sentiment analysis, unresolved ticket resolution
   
   ---
   
   # Supercharge Support with AI: Automatically Triage and Close Long-Lived Jira Issues Using n8n
   
   Managing customer support tickets can be a daunting task—especially when older or unresolved issues begin to pile up. Whether due to staff shortages, shifted priorities, or sheer oversight, “zombie issues” often persist in your Jira backlog, cluttering your queues and diminishing team efficiency. 
   
   With n8n, you can build robust, automated workflows that combine the power of AI, sentiment analysis, and third-party tools like Jira, Notion, and Slack to automatically identify and resolve long-unattended tickets—saving time, improving customer satisfaction, and increasing your support team’s capacity. This article walks through such a workflow using n8n and the GPT-4o language model.
   
   ---
   
   ## Workflow Overview
   
   This n8n workflow performs the following critical tasks:
   
   1. **Identifies Long-Lived, Unresolved Jira Tickets**  
      It starts with a Schedule Trigger (typically daily), executing a Jira JQL query to retrieve issues in “To Do” or “In Progress” status that are over 7 days old.
   
   2. **Creates One Subworkflow per Issue**  
      Each issue is funneled into an `Execute Workflow` node to run them individually and in parallel—enhancing performance.
   
   3. **Fetches and Prepares Issue Data**  
      The workflow gathers metadata and comments from the Jira issue, which are then converted into a single, AI-readable thread. This context is key for downstream AI analysis.
   
   4. **Uses AI to Classify the Issue State**  
      GPT-4o via the `Text Classifier` node evaluates whether the issue is resolved, pending further information, or awaiting a response.
   
   ---
   
   ## AI-Powered Ticket Resolution Paths
   
   Based on the classification, the workflow follows three main paths, each enhanced with AI intelligence:
   
   ### 1. ✅ Issue Appears Resolved  
   The workflow checks for positive sentiment using the `Sentiment Analysis` node. Depending on detected sentiment:
   
   - If positive, it sends a polite closure message and asks for a customer review.
   - If negative, it notifies a Slack channel to escalate for further human action before closing.
   
   ### 2. 🤖 No Team Response Yet? Try Autoresolution  
   The `KnowledgeBase Agent` (powered by GPT-4o) explores two tools:
   - Similar issues from Jira (via AI-enabled JQL search)
   - Notion pages acting as a knowledge base
   
   If a solution is found, it's added as a Jira comment, and the ticket is closed. If a solution can't be derived, the user is proactively informed of this outcome.
   
   ### 3. ⏳ Still Waiting On User?
   If the last comment wasn't from the bot and a human response is pending, an AI summarizer (`Issue Reminder Agent`) crafts a tailored reminder and posts it to the issue.
   
   ---
   
   ## Smart AI Integration 
   
   This workflow effectively functions as a support agent—analysing context, offering informed recommendations, and resolving tickets or escalating when necessary.
   
   AI nodes used:
   - 🧠 GPT-4o via `lmChatOpenAi` for classification and summarization
   - 🧠 LangChain Text Classifier for intelligent path selection  
   - 🧠 Sentiment Analysis for assessment of user satisfaction
   
   The final output includes automated messages directly inside Jira issues and Slack notifications sent to relevant support channels for cases requiring human intervention.
   
   ---
   
   ## Benefits of Automating Ticket Resolution
   
   - 💼 Increases support team bandwidth by auto-resolving simple and forgotten tickets
   - 🧙 Acts as a virtual support agent through GPT-4o
   - 📉 Reduces customer dissatisfaction by proactively addressing neglected issues
   - 💬 Closes feedback loops through reminders and resolution messages
   - 📊 Includes Slack alerts for human oversight where needed
   
   ---
   
   ## Third-Party APIs and Tools Used
   
   Here are the systems the workflow connects with:
   
   1. **Jira Software Cloud API**  
      Used for retrieving, updating, and commenting on Jira tickets.
      
   2. **OpenAI (GPT-4o-mini)**  
      Processes and classifies issue discussions to determine resolution status and generate messages.
      
   3. **Notion API**  
      Acts as a searchable knowledge base for issue resolution attempts.
   
   4. **Slack API**  
      Posts notifications and escalations to internal team channels when human review is needed.
   
   ---
   
   ## Try It Yourself
   
   Whether you're looking to automate support flows or reduce backlog friction, this n8n template is a powerful starting point. It lets you engage AI as a force multiplier—without writing a single line of code!
   
   You can find and adapt the template directly from [n8n's community](https://community.n8n.io/) or join their [Discord](https://discord.com/invite/XPKeKXeB7d) for help.
   
   ---
   
   Start reducing your Jira backlog today—one smart bot at a time.

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.