This article provides a complete, practical walkthrough of the Code Filter Send 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

   Title:
   Building a Smart AI-Powered Customer Insights Tool Using n8n, GPT-4, and Google Sheets
   
   Meta Description:
   Discover how to create a powerful AI agent in n8n that enriches customer support and analytics by integrating GPT-4 with Google Sheets. Learn how this no-code workflow dynamically queries customer data using modular tools.
   
   Keywords:
   n8n, GPT-4, OpenAI, Google Sheets, AI agent, customer data, no-code automation, LangChain, workflow automation, AI workflow, AI tools, API integration
   
   Third-party APIs Used:
   
   - OpenAI API (via n8n LangChain GPT-4o-mini chat integration)
   - Google Sheets API (via n8n Google Sheets OAuth2 integration)
   
   Article:
   
   In today’s fast-paced digital business environments, companies often deal with large datasets across multiple tools. Making this data actionable—such as answering customer support queries or identifying top customers—can be tedious and time-consuming. But what if a conversational AI agent could handle this for you?
   
   That’s exactly what this n8n workflow does: it creates a smart AI agent powered by OpenAI’s GPT-4, designed to answer customer-related questions by dynamically querying a Google Sheet containing customer data. In this article, we'll explore how this sophisticated setup works, how it leverages modular tools for flexibility, and how you can adapt it to your own needs.
   
   Overview: What This Workflow Does
   
   This n8n workflow consists of a main AI agent and a modular sub-workflow system. The agent can respond to user queries—such as “Which is our biggest customer?”—by calling one or more sub-tools that query specific parts of a Google Sheet. This approach has several advantages:
   
   - Avoids overwhelming the AI with too much data
   - Enables targeted, efficient queries
   - Reduces API usage and improves performance
   - Scales easily across datasets and business use cases
   
   Let’s dive into the key components.
   
   1. AI Agent as an Orchestrator
   
   At the core of this workflow is the AI Agent node (`AI Agent`) that uses the OpenAI GPT-4o-mini model. It’s designed as a LangChain-compatible agent, which means it can intelligently decide which tool(s) to call based on the question it receives.
   
   The AI agent is triggered via a chat frontend or webhook (`When chat message received`), where users can type natural language queries. For instance, a query like "Who is our biggest customer?" could lead the agent to first list all column names, retrieve customer revenue data, and then identify the row with the highest value.
   
   2. Powered by GPT-4 via OpenAI
   
   The backbone of the AI’s natural language capability comes from the `OpenAI Chat Model1` node. It uses the GPT-4o-mini model via OpenAI’s API to parse user intent, understand context, and plan calls to sub-tools. The output from this model is then plugged back into the agent's reasoning loop.
   
   3. Memory Buffering for Context
   
   To maintain the conversational thread, the workflow includes a `Simple Memory` node—essentially a buffer memory that helps the AI remember what has been said. This is especially useful in multi-turn interactions or follow-up queries, mimicking a real assistant.
   
   4. Modular Tool Workflows
   
   The most exciting part of this system is its use of modular tools built as reusable sub-elements via the `toolWorkflow` node type. These tools include:
   
   - list_columns: Returns all column names in the dataset. Ideal for initial exploration and validation.
   - column_values: Retrieves values for a specific column across all customers. For example, fetching all revenue values.
   - get_customer: Fetches all data for a specific customer, based on row_number. Ensures full contextual understanding of one customer.
   
   Each tool is lightweight and focused, making them easily reusable in other contexts or agents. They all rely on a sub-workflow that reads from a centralized Google Sheet hosted at a configurable URL.
   
   5. Sub-workflow to Query Google Sheets
   
   The actual data-handling logic resides in a dedicated sub-workflow triggered by the `When Executed by Another Workflow` node. Here’s what happens under the hood:
   
   - Step 1: `Set Google Sheet URL` allows dynamic URL placement (useful for switching datasets).
   - Step 2: `Get Google Sheet contents` uses n8n’s native Google Sheets node to pull data from a specified sheet.
   - Step 3: `Check operation` switch node routes the logic based on what's needed (column names, specific values, or rows).
   - Step 4: Downstream nodes like `Get column names`, `Prepare column data`, and `Filter` clean and structure the output as requested.
   
   The final results are returned in JSON format, compact and optimized for parsing by GPT models.
   
   6. Highly Customizable
   
   One of the key design benefits of this workflow is its modularity:
   
   - You can change out the Google Sheets URL from a single node.
   - New tools can easily be created by replicating and modifying existing patterns.
   - The agent’s behavior can be extended with new logic or different LLM providers.
   
   How to Try It
   
   If you’re using a platform that supports chatbot interactions with n8n (like via a custom UI), simply start the workflow and type in queries such as:
   
   “Which is our biggest customer?”
   
   The agent will handle the logic: calling the column listing tool, identifying the revenue column, pulling customer values, and selecting the top customer. All of this without needing to write complex scripts or integrations manually.
   
   Use Cases
   
   While this workflow is designed around customer data, it can easily be adapted for other domains:
   
   - Employee data analytics
   - Sales and lead management
   - Product trend analysis
   - Support ticket triage
   
   Conclusion
   
   This n8n-powered AI agent showcases the power of combining LLMs with structured data in a modular and scalable way. By using GPT-4 to parse intent and delegating execution to focused tools, you create an intelligent assistant that is both powerful and cost-effective.
   
   Whether you’re in marketing, sales, support, or operations, integrating such a system may dramatically improve the way your team interacts with internal data—by simply asking questions in plain English.
   
   Start building your own AI workflows in minutes with n8n—and let creativity and data work seamlessly together.
   
   — Written by your AI Assistant.

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.