Old Jira tickets can slow teams down. This automation hunts for unresolved issues that have been open for seven days or more, then decides the next best step. Support leaders use it to clear backlogs, keep customers updated, and protect response quality.

On a daily schedule, it pulls a list of long lived issues and runs each one in parallel for speed. It gathers every comment, builds a clean thread, and sends the summary to an AI model that classifies the ticket state. When a fix is already reached, it posts a closing note, checks sentiment, and either asks for a review or alerts a Slack channel if the customer is unhappy. If no teammate has replied, an AI agent searches similar Jira cases and Notion pages to draft a clear answer, then comments and closes. The agent uses a structured output parser to keep fields like solution found, short summary, and response clean and easy to audit. If the case is waiting on someone, a reminder is posted, and a guard prevents spam when the last message was from a bot.

Setup needs connections for Jira Software Cloud, OpenAI, Notion, and Slack. Adjust your age filter, JQL, Done status ID, and the Slack channel. Expect strong time savings on ticket triage, fewer stale threads, and faster recovery on negative cases. Start with one project to tune prompts and messages, then roll out across teams.

• Daily schedule scans for unresolved Jira issues older than seven days
• Parallel processing with Execute Workflow to speed up large queues
• Collects and joins all Jira comments to build a full conversation history
• AI classification of ticket state using an OpenAI chat model
• Sentiment check on resolved threads with Slack escalation for negative cases
• Knowledge base agent searches similar Jira issues and Notion pages to draft solutions
• Structured output parser enforces clean fields like solution found and response
• Reminder agent posts clear follow ups only when the last message is not from a bot
• Automatic comments to ask for feedback, add autoclose notes, and set status to Done

• Reduce manual review of aging tickets from hours to minutes each day
• Automate up to 80 percent of stale ticket handling with AI and clear rules
• Escalate unhappy resolutions to Slack within minutes for quick recovery
• Handle 5x more backlog items in parallel without extra headcount
• Improve closing note quality and consistency with AI generated replies
• Connect Jira, Notion, Slack, and OpenAI in one coordinated flow

1. Import the template into n8n: Create a new workflow in n8n > Click the three dots menu > Select 'Import from File' > Choose the downloaded JSON file.
2. You'll need accounts with Jira Software Cloud, Slack, Notion and OpenAI. See the Tools Required section above for links to create accounts with these services.
3. In the n8n credentials manager, open any Jira node (for example Get List of Unresolved Long Lived Issues), choose 'Create new credential', then follow the on screen steps. Use your Atlassian site URL, your email, and an API token from id.atlassian.com under Security API tokens.
4. Open any OpenAI Chat Model node, select 'Create new credential', and paste your OpenAI API key from your OpenAI account dashboard.
5. For Notion, open the Query KnowledgeBase node, click 'Create new credential', and connect your Notion integration. If using a token, create an internal integration in Notion and share your pages or databases with that integration.
6. For Slack, open the Notify Slack Channel node, click 'Create new credential', authorize your workspace, and select the channel where alerts should post. Invite the bot to the channel if messages do not appear.
7. Edit the Get List of Unresolved Long Lived Issues node to match your projects and age rule. Adjust the JQL or filter to target issues older than seven days and unresolved.
8. Check the Close Issue and Close Issue2 nodes. Confirm the Done status ID matches your Jira workflow. If it differs, select the correct status from the list.
9. Review and customize the Ask For Feedback Message, Add Autoclose Message, and Reply to Issue text to fit your tone and policy.
10. Ensure the KnowledgeBase Agent has both tools connected: Find Similar Issues in Jira and Query KnowledgeBase in Notion. Point the Notion tool at the right pages or databases.
11. Test safely: Temporarily change the Schedule Trigger to a manual run, execute the flow on a small set, and confirm Jira comments, status changes, and Slack alerts appear as expected.
12. Troubleshooting: If Jira returns 401, recheck the site URL, email, and API token. If Notion returns empty results, share the pages with your integration. If Slack does not post, invite the app to the channel. If status updates fail, correct the status ID. If you hit rate limits, reduce parallel runs or add delays. If AI outputs are empty, refine prompts in the classification and agent nodes.