This article provides a complete, practical walkthrough of the Splitout Schedule Create 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:
   Automated JIRA Ticket Assignment Using AI Embeddings and Supabase Vector Search in n8n
   
   Meta Description:
   Discover how to automate stale JIRA ticket assignment using OpenAI embeddings and Supabase’s vector store with n8n. This AI-powered workflow identifies the best team member for each issue by analyzing resolved tickets for contextual similarities.
   
   Keywords:
   n8n workflow, JIRA automation, AI ticket assignment, Supabase vector store, OpenAI embeddings, LangChain, auto-assign JIRA issues, stale tickets, JIRA project management, productivity automation, GPT-4o-mini
   
   Third-Party APIs Used:
   
   1. Jira Software Cloud API – for retrieving and updating issues from JIRA.
   2. OpenAI API – to generate embeddings and power GPT-4o-mini chat completions.
   3. Supabase API – for storing and retrieving vector embeddings of past JIRA issues, using pgvector.
   4. LangChain (via n8n LangChain plugin) – to handle AI components, including embedding generation, vector search, agents, and AI prompts.
   
   Article:
   
   Ensure No JIRA Ticket Falls Through the Cracks with This AI-Powered n8n Workflow
   
   In fast-paced development environments, it’s all too common for JIRA issues to remain unassigned and get buried under an ever-growing backlog. With this intelligent n8n automation template leveraging OpenAI, Supabase, and LangChain, you can reduce manual triaging and ensure every issue is assigned to the right person — before it goes stale.
   
   This article covers an advanced yet easy-to-use automation built on n8n that finds unassigned JIRA tickets older than five days, identifies team members who've solved similar problems in the past, checks their current workloads, and auto-assigns the issue accordingly — all using AI.
   
   Here's a detailed look at how the system works and what makes it so powerful.
   
   Step 1: Build an AI-Searchable Knowledge Base from Resolved Issues
   
   The first flow in this automation scans your JIRA project for recently resolved issues. It performs the following:
   
   - Pulls resolved JIRA issues from the last 24 hours.
   - Cleans duplicate entries that may already exist in the system.
   - Embeds each issue using OpenAI’s Embeddings API.
   - Loads them into Supabase’s vector store using LangChain.
   
   Each stored issue includes rich metadata — project key, issue type, creation date, resolution date, assignee ID, name, title, and description. This transforms your past JIRA work into a smart, searchable knowledge base for machine learning comparisons.
   
   Step 2: Identify Stale, Unassigned Issues
   
   The system runs a second scheduled n8n trigger that checks JIRA for any issues matching the following criteria:
   
   - Status is “To Do”
   - Assignee is empty
   - The issue has remained unassigned for more than 5 days
   
   These stale tickets are then passed one by one through the AI pipeline for evaluation.
   
   Step 3: Find Similar Issues Resolved in the Past
   
   Each stale ticket is converted into a search query by embedding its title and description using OpenAI and looking it up within the Supabase vector store. This reveals up to 20 closest-matching resolved issues.
   
   LangChain’s Agent and Tooling framework then processes this data using GPT-4o-mini with a specific system prompt. The AI returns the assignee_id and assignee_name from the best-matching resolved tickets — building a pool of potential candidates based on contextual experience.
   
   Step 4: Calculate Team Member Capacity
   
   With a shortlist of knowledgeable team members in hand, the automation now checks how many other issues each candidate is currently assigned to via the JIRA API.
   
   Using n8n’s summarize and sort nodes, it calculates how many open/in-progress issues are held by each user and sorts them to identify the team member with the most current capacity.
   
   Step 5: Auto-Assign the Issue and Leave a Note
   
   Finally, the system updates the JIRA issue, assigning it to the most suitable and available team member. It also posts an inline comment, noting that the ticket was auto-assigned due to prolonged inactivity, minimizing confusion and bringing transparency to the process.
   
   What Happens When No Similar Issue is Found?
   
   If none of the historic issues are relevant — or if no vector matches are retrieved — the automation safely skips that issue. This keeps the system reliable and ensures no poor assignments are made blindly. (You could also extend it to notify a project manager.)
   
   Why This Workflow Matters
   
   - 🔁 Continuous auto-updating knowledge base of resolved tickets
   - 🔎 Intelligent similarity matching using OpenAI embeddings
   - 🧠 Context-aware team assignments via vectorized memory
   - ⚖️ Workload balancing so no one is overloaded
   - ✅ No more unassigned issues going stale and bottlenecking delivery
   
   Conclusion
   
   This n8n workflow combines the power of OpenAI, Supabase’s pgvector database, and LangChain to transform traditional JIRA issue management into a smart, self-operating system. Whether you're a dev team lead, PM, or part of an ops team, this kind of automation significantly reduces manual labor — all while making smarter assignments with confidence.
   
   Want to Try it Out?
   
   Download the template directly in n8n, plug in your JIRA, Supabase, and OpenAI credentials, and follow the documentation links referenced in each sticky note within the visual editor.
   
   For support or customization:
   - Join the n8n Discord
   - Ask questions on the n8n Community Forum
   
   Happy automating!

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.