Customer Support AI Agent

About AI workflows

An AI workflow chains several tools together so that the output of one becomes the input of the next. Instead of moving text by hand between a research tool, a writing tool and an image generator, the workflow runs the sequence for you and returns the finished result.

The workflows here are pre-built for jobs that genuinely take several steps: turning a topic into a researched article, converting a long video into a set of social posts, or taking a product description through copy, images and ad variants. Each one shows the steps it runs and which tools it uses before you start it.

How to use it

Pick a workflow

Browse the available workflows and open the one matching your task. Each shows its steps and expected output up front.

Provide the input

Give the workflow its starting material — a topic, a URL, a document or a brief.

Review each stage

The workflow surfaces intermediate output as well as the final result, so you can see where a run went wrong and re-run just that stage.

Frequently asked questions

How is a workflow different from a single prompt?

A prompt is one call to one model. A workflow is a sequence of calls where each step is specialised and receives the previous step's output, which produces better results on multi-stage tasks than asking one model to do everything at once.

Can I edit a workflow's steps?

The pre-built workflows run a fixed sequence. If you need a different chain, the n8n templates library is designed for building custom automations.

How long does a workflow take to run?

Most complete in under a minute. Workflows that involve image generation or long documents take longer, because each stage waits for the previous one to finish.

Do I need an account?

Browsing workflows is open to everyone. Running one requires an account so that usage can be attributed and rate-limited fairly.

Step-by-Step Implementation

Phase 1: Set up the Ingestion Trigger

Step 1

First, we need our agent to listen for incoming support requests. We will use n8n to connect to your Gmail or Zendesk.

Open n8n

Instructions

1. Log into your n8n workspace.
2. Add a new **Gmail Trigger** node or **Webhook** node.
3. Configure it to trigger on "Message Received".
4. Extract the `subject`, `sender`, and `body_text`.

Phase 2: Connect Knowledge Base (RAG)

Step 2

To prevent hallucinations, the agent needs context. We fetch related articles from a Vector Store.

Open Pinecone

Code Snippet

// Sample Python script to populate your Pinecone DB with FAQ data
import pinecone
from langchain.embeddings.openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
index = pinecone.Index("support-kb")
index.upsert(vectors=[
    ("faq-1", embeddings.embed_query("Refund policy: 30 days..."), {"category": "billing"}),
])

Phase 3: The System Prompt

Step 3

Configure the AI node with a strict system prompt that enforces brand voice and restricts capabilities.

Open OpenAI

System Prompt

You are an empathetic, professional Customer Support Agent for [Your Company].
Your goal is to answer the customer's query using ONLY the provided context from our knowledge base.

Context: {{ $json.knowledge_base_results }}
Customer Email: {{ $json.body_text }}

Rules:
1. If the answer is not in the context, apologize and say a human agent will follow up.
2. Never promise refunds unless explicitly authorized by the context.
3. Keep the tone friendly and concise.

Phase 4: Draft or Send

Step 4

Connect the output of the OpenAI node to an email sender or ticket updater.

Open Zendesk

Instructions

1. Add a **Zendesk Node** (or Gmail node).
2. Set the Action to "Update Ticket".
3. Map the AI's response to the Internal Note or Public Reply field.
4. Activate the workflow!

Step-by-Step Implementation

Phase 1: Set up the Ingestion Trigger

Step 1

First, we need our agent to listen for incoming support requests. We will use n8n to connect to your Gmail or Zendesk.

Open n8n

Instructions

1. Log into your n8n workspace.
2. Add a new **Gmail Trigger** node or **Webhook** node.
3. Configure it to trigger on "Message Received".
4. Extract the `subject`, `sender`, and `body_text`.

Phase 2: Connect Knowledge Base (RAG)

Step 2

To prevent hallucinations, the agent needs context. We fetch related articles from a Vector Store.

Open Pinecone

Code Snippet

// Sample Python script to populate your Pinecone DB with FAQ data
import pinecone
from langchain.embeddings.openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
index = pinecone.Index("support-kb")
index.upsert(vectors=[
    ("faq-1", embeddings.embed_query("Refund policy: 30 days..."), {"category": "billing"}),
])

Phase 3: The System Prompt

Step 3

Configure the AI node with a strict system prompt that enforces brand voice and restricts capabilities.

Open OpenAI

System Prompt

You are an empathetic, professional Customer Support Agent for [Your Company].
Your goal is to answer the customer's query using ONLY the provided context from our knowledge base.

Context: {{ $json.knowledge_base_results }}
Customer Email: {{ $json.body_text }}

Rules:
1. If the answer is not in the context, apologize and say a human agent will follow up.
2. Never promise refunds unless explicitly authorized by the context.
3. Keep the tone friendly and concise.

Phase 4: Draft or Send

Step 4

Connect the output of the OpenAI node to an email sender or ticket updater.

Open Zendesk

Instructions

1. Add a **Zendesk Node** (or Gmail node).
2. Set the Action to "Update Ticket".
3. Map the AI's response to the Internal Note or Public Reply field.
4. Activate the workflow!

About AI workflows

How to use it

Pick a workflow

Provide the input

Review each stage

Frequently asked questions

How is a workflow different from a single prompt?

Can I edit a workflow's steps?

How long does a workflow take to run?

Do I need an account?

Prerequisites

Agent Architecture

Step-by-Step Implementation

Phase 1: Set up the Ingestion Trigger

Phase 2: Connect Knowledge Base (RAG)

Phase 3: The System Prompt

Phase 4: Draft or Send

Customer Support AI Agent

Prerequisites

Agent Architecture

Step-by-Step Implementation

Phase 1: Set up the Ingestion Trigger

Phase 2: Connect Knowledge Base (RAG)

Phase 3: The System Prompt

Phase 4: Draft or Send