LangChain Automate
LangChain Automate streamlines customer support by automatically managing unresolved Jira issues. It identifies long-lived tickets, retrieves comments, and classifies their status using AI. The workflow sends reminders for pending actions, attempts to resolve issues using knowledge bases, and closes tickets while ensuring customer satisfaction through sentiment analysis. This automation enhances efficiency, reduces response times, and improves overall service quality.
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Workflow Overview
LangChain Automate streamlines customer support by automatically managing unresolved Jira issues. It identifies long-lived tickets, retrieves comments, and classifies their status using AI. The workflow sends reminders for pending actions, attempts to resolve issues using knowledge bases, and closes tickets while ensuring customer satisfaction through sentiment analysis. This automation enhances efficiency, reduces response times, and improves overall service quality.
Target Audience
- Customer Support Teams: Those looking to automate the handling of long-lived unresolved JIRA issues, improving response times and customer satisfaction.
- Project Managers: Professionals who need to keep track of unresolved issues and ensure timely follow-ups.
- Developers: Individuals who want to integrate AI tools into their workflow for enhanced issue resolution.
- Automation Enthusiasts: Users interested in leveraging automation tools like n8n to streamline processes and reduce manual efforts.
Problem Solved
This workflow addresses the challenge of managing unresolved JIRA issues that have been inactive for more than 7 days. It automates the process of:
- Identifying long-lived issues.
- Retrieving comments and metadata to assess the state of the issue.
- Classifying issues to determine if they can be resolved or need further action.
- Sending reminders to users for pending actions and notifying relevant team members in case of unresolved issues.
Workflow Steps
- Schedule Trigger: Runs daily to check for unresolved JIRA issues that are older than 7 days.
- Get List of Unresolved Long Lived Issues: Fetches the list of issues that meet the criteria.
- Execute Workflow for Each Issue: Each issue is processed in parallel to enhance performance.
- Get Issue Metadata: Retrieves essential information about the issue, including the reporter and description.
- Get Issue Comments: Gathers all comments related to the issue for context.
- Join Comments: Aggregates comments into a simplified format for AI processing.
- Classify Current Issue State: Uses AI to determine if the issue is resolved, pending more information, or still waiting for a response.
- Customer Satisfaction Assessment: Analyzes sentiment to gauge user satisfaction and decide on follow-up actions.
- KnowledgeBase Agent: Attempts to resolve the issue using existing knowledge resources.
- Notify Slack Channel: Sends notifications to the relevant Slack channel regarding unresolved issues or actions taken.