Intelligent Jira Issue Assignment & Rescheduling
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Automate Jira issue assignment by leveraging AI to find similar resolved issues and their assignees. The workflow intelligently suggests the best team member to handle new or complex tasks. It also allows for proactive rescheduling of tasks to prevent bottlenecks.
About This Workflow
This n8n workflow revolutionizes Jira issue management by intelligently assigning tasks. It begins by converting incoming Jira issues into embeddings using OpenAI, storing them in a Supabase vector database. When a new issue arises, the workflow queries the database for similar resolved issues, identifying the individuals who previously tackled them. An OpenAI agent analyzes this information and the issue details to suggest the most suitable assignee. The system then checks for existing in-progress tasks for the suggested assignee and can proactively reschedule tasks if needed. This ensures efficient workload distribution and timely resolution of Jira tickets, preventing issues from becoming stale or creating bottlenecks.
Key Features
- AI-Powered Assignment: Leverages OpenAI to analyze issue context and suggest the most qualified assignee based on historical data.
- Vector Database Integration: Utilizes Supabase for efficient storage and retrieval of issue embeddings, enabling fast similarity searches.
- Proactive Rescheduling: Checks for assignee workload and can automatically reschedule tasks to avoid delays.
- Automated Data Ingestion: Converts Jira issue details into rich embeddings with relevant metadata.
- Jira Integration: Seamlessly interacts with Jira for issue retrieval, updating, and assignment.
How To Use
- Configure Triggers: Set up the
Schedule Triggernodes to define when the workflow should run (e.g., to process new issues or check for overdue tasks). - Connect Jira: Ensure your Jira account is authenticated via the
jiraSoftwareCloudApicredential. - Set up OpenAI: Configure your OpenAI API key using the
openAiApicredential. - Configure Supabase: Set up your Supabase connection with the
supabaseApicredential, ensuring you have adocumentstable for storing issue embeddings. - Define Data Loading: Customize the
Default Data Loadernode to map your Jira issue fields to relevant metadata for embedding. - Tune Text Splitting: Adjust the
Recursive Character Text Splitterparameters to optimize how issue text is segmented for embedding. - Configure Agent Prompting: Modify the
Find Similar Issues + Assigneesnode's system message and prompt to refine the AI's behavior and desired output format. - Map User Check Logic: Update the
Check User Workflownode's JQL to accurately reflect how you want to check for existing tasks for potential assignees. - Review and Test: Thoroughly test the workflow with various Jira issue scenarios to ensure accurate assignments and rescheduling.
Apps Used
Workflow JSON
{
"id": "6e1f83b4-520a-42ab-9eb8-0362501e88d0",
"name": "Intelligent Jira Issue Assignment & Rescheduling",
"nodes": 19,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 6e1f83b4-520a...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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