This article provides a complete, practical walkthrough of the Googlebigquery Stickynote Automate Triggered 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

   Sure! Here's a complete short-form article based on the n8n workflow you provided.
   
   ---
   
   📌 Title:  
   Build an AI-Powered SQL Assistant for Supply Chain Analytics Using n8n and OpenAI
   
   📋 Meta Description:  
   Learn how to create an AI-driven SQL query chatbot in n8n that connects with OpenAI and Google BigQuery for real-time insights into supply chain performance.
   
   🔑 Keywords:  
   n8n, OpenAI, AI agent, BigQuery, SQL automation, chatbot workflow, supply chain analytics, AI assistant, LangChain, process automation, data visualization, natural language to SQL
   
   🧰 Third-Party APIs and Integrations Used:
   
   - OpenAI GPT-4o Mini (via OpenAI API)
   - Google BigQuery (via Google Cloud Platform API)
   
   ---
   
   📝 Article:
   
   In today’s fast-paced business environment, quick access to actionable data is critical—especially in logistics and supply chain management. But querying large datasets often requires technical expertise that many business stakeholders simply don’t have. Wouldn’t it be great if stakeholders could just “chat” with your supply chain database and get instant answers?
   
   Thanks to n8n and a combination of OpenAI's GPT models and Google BigQuery, you can now build a conversational SQL assistant tailored specifically for supply chain analytics.
   
   In this article, we'll break down a real n8n workflow that lets users interact via a chat interface, request logistics metrics in natural language, and receive instant SQL-powered insights—no code or database skills required.
   
   ---
   
   🧠 Overview of the Workflow
   
   This intelligent system integrates the following key components:
   
   1. 🚀 Chat Trigger Node  
      The conversation begins with a trigger node called "Chat with the User." This node represents the entry point for users—via web, Slack, Telegram, or other messaging platforms. Users submit questions like:  
      "How many shipments were delayed last month?" or  
      "Show me delivery stats by city."
   
   2. 🤖 AI Control Tower Agent – Your Custom SQL Assistant  
      At the heart of this workflow is the “AI Control Tower Agent,” a LangChain agent node configured with an OpenAI GPT model. This AI is given a very detailed system prompt:
      - It is instructed to act as a supply chain SQL expert.
      - It has restricted behavior to avoid revealing raw SQL and to provide responses strictly in clean, relevant data formats.
      - It uses a tool called `bigquery_tool` to send structured SQL to BigQuery only when necessary.
   
   3. 🛠️ LangChain Tool Workflow Node – Safely Executing SQL  
      The AI doesn’t execute SQL directly. Instead, it hands off a cleaned, structured query to a specialized node: “Call Query Tool.” This node:
      - Routes the SQL to another n8n process specifically created for querying BigQuery.
      - Makes sure the query is properly sanitized (stripping formatting like ```sql) to avoid execution errors.
   
   4. 🧹 Query Cleanup and Execution Workflow  
      The separate BigQuery workflow includes:
      - A trigger that receives the SQL query.
      - A Code node that sanitizes the input.
      - A “Query Database” node using Google BigQuery to execute that SQL safely.
      - Through proper modular design, this workflow can be shared and reused by multiple agents or chatbot applications across an organization.
   
   5. 📦 Return & Present Results  
      Once the raw results are received, the AI Agent packages them in a clean, user-friendly response:
      - Metrics are shared numerically (“There were 5,432 delayed shipments in the last 21 days.”).
      - Tabular data is formatted neatly, showing a sample of up to 10 rows (or more if requested).
   
   ---
   
   📊 Why This Matters for Supply Chain Analytics
   
   Supply chains are rich with data—but often trapped behind SQL queries and dashboards. This AI-enhanced workflow bridges that gap by empowering users to ask complex questions in natural language while maintaining the integrity, performance, and security of your data systems.
   
   Here’s what makes this approach particularly effective:
   - 🧩 Modular: You can update the AI prompt or swap out tools without redoing the entire setup.
   - 🔒 Secure: Raw SQL input is sanitized before running on live datasets.
   - 📈 Scalable: Since you separate the BigQuery tool into its own reusable workflow, it can serve many use cases.
   - 🗣️ Conversational: Anyone in procurement, logistics, or operations can now “talk” to their data.
   
   ---
   
   📋 Getting Started with the Setup
   
   Want to build your own? Here's a quick checklist:
   
   ✅ Step 1: Create a new n8n workflow with a Chat Trigger node.  
   ✅ Step 2: Add an AI Agent with the GPT-4o Mini model and customize the system message.  
   ✅ Step 3: Build a reusable query executor workflow using Google BigQuery.  
   ✅ Step 4: Use the Langchain ToolWorkflow node to connect your agent to the BigQuery runner.  
   ✅ Step 5: Test your chatbot and iterate on query structure, result formatting, and user guidance.
   
   Learn more and watch a step-by-step tutorial here:  
   🎥 [Watch the tutorial](https://www.loom.com/share/50271f9d50214d7184830985497a75ec?sid=d0c410dc-29f1-488f-b89a-4011de0ded07)
   
   ---
   
   🧠 Final Thoughts
   
   With no-code automation from n8n, powerful LLMs from OpenAI, and scalable analytics from BigQuery, you can equip your organization with a smart assistant purpose-built for analytics—without writing a single line of frontend code or building out complicated dashboards.
   
   This workflow is a game changer for supply chain teams looking to unlock insights quickly and intuitively.
   
   Happy building! 🚚📦💡
   
   --- 
   
   Would you like this rewritten as a LinkedIn post, tutorial, or landing page description?

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.