AI-Powered Jira Issue Auto-Assignment from Spreadsheets - n8n Workflow

Development Managers & Scrum Masters: Struggling with manually assigning incoming tasks and bugs to the right expert.
Product Owners: Needing rapid, accurate triage and assignment immediately after task creation.
n8n Automation Specialists: Looking for advanced n8n templates that combine RAG (Retrieval Augmented Generation) techniques with task management systems.
DevOps Teams: Seeking to automate the creation and assignment of incident reports or tasks directly into Jira.

Manual task assignment is slow, error-prone, and often leads to engineers working outside their core expertise. This advanced n8n workflow solves this by using AI to analyze incoming tasks, match them against an employee expertise roster stored in Google Sheets, and automatically create the assigned ticket in Jira.

This setup utilizes an n8n trigger to initiate the process when a new task is logged in the sheet. The logic is managed by an AI Agent n8n node powered by Azure OpenAI (GPT-4o-mini), ensuring that the assignment rules—including complex scoring based on exact, close, and semantic matches—are applied perfectly every time. This robust n8n workflow significantly accelerates the triage process, making it a critical asset for teams utilizing modern n8n templates for operational efficiency.

This automation begins with the primary n8n trigger:

1. Trigger Initiation: The Google Sheets Trigger n8n node constantly monitors a designated spreadsheet for a new row added (representing a new task entry, including 'Task Name' and 'Related Area').


2. AI Analysis and Retrieval: The data is passed to the AI Agent n8n node. This agent, configured with detailed rules, performs two key actions:

1. Structured Output: The AI Agent uses the Structured Output Parser n8n node to ensure the result is a clean JSON object containing the chosen employeeid, taskname, and crucially, the itemtype ("bug" or "task").


2. Flow Routing: The Switch n8n node evaluates the itemtype determined by the AI. This allows the n8n workflow to handle different issue types appropriately.


3. Jira Issue Creation: Depending on the route, one of two Jira n8n node actions is executed: either creating a 'Bug' type issue or a 'Task' type issue, both automatically assigning the ticket using the employee_id provided by the AI.

To deploy this powerful n8n workflow using these n8n templates, follow these steps:

1. Import: Copy the provided JSON code and paste it into your n8n instance using the 'New' -> 'Import from JSON' function.


2. Google Sheets Setup (Trigger): Configure the Google Sheets Trigger n8n node. You will need to select the sheet that serves as your task intake queue and connect your Google Sheets credentials.


3. Google Sheets Setup (Roster Tool): Configure the Get row(s) in sheet in Google Sheets n8n node. This must point to the spreadsheet containing your employee roster (Name, Expertise, ID). Ensure the credentials have read access.


4. Azure OpenAI Credentials: Set up the credentials for the Azure OpenAI Chat Model n8n node. This requires the Azure API key and base URL.


5. Jira Credentials: Configure both Jira Create an issue n8n nodes. Connect your Jira Software Cloud API credentials and select the target Project and corresponding Issue Types (Bug and Task) used in your instance.


6. Activate: Save the n8n workflow and set it to 'Active' to start monitoring the Google Sheet for new tasks.

Google Sheets Trigger: The initial n8n trigger node. Watches the task intake sheet for newly added rows, initiating the n8n workflow upon detection.
AI Agent (LangChain): The central decision-making n8n node. It orchestrates the assignment logic using a detailed system prompt (acting as an AI Assignment Agent) and external tools (Google Sheets Roster).
Azure OpenAI Chat Model: The underlying Large Language Model (GPT-4o-mini) used by the AI Agent for executing the complex retrieval and scoring logic defined in the n8n node's system message.
Get row(s) in sheet in Google Sheets: Configured as a tool accessible to the AI Agent. It fetches the complete employee expertise roster data, which the AI uses as its knowledge base.
Structured Output Parser: Ensures the AI's complex reasoning results in a reliable, parsable JSON format, guaranteeing data consistency for downstream n8n nodes.
Switch: A core logic n8n node that routes the execution based on the AI-determined itemtype ('bug' or 'task').

• Create an issue / Create an issue1 (Jira): These n8n nodes finalize the process. They create the appropriate Jira issue type (Bug or Task) and dynamically set the assignee using the employeeid derived from the AI Agent's structured output.