Artificial Intelligence (AI) has transformed the way businesses operate, streamlining processes, improving decision-making, and enhancing customer experiences. Among the various AI-powered solutions, AI Agents stand out as intelligent, automated systems capable of performing complex tasks with minimal human intervention. In this article, we'll explore what AI Agents are, how they compare to Large Language Models (LLMs), where they can be used in business, and how you can build a no-code AI Agent using n8n.io.

How AI Agents Differ from Large Language Models (LLMs)

Although AI Agents and Large Language Models (LLMs) both leverage artificial intelligence, they serve different purposes and function differently:

1. Functionality: AI Agents focus on automating workflows, making decisions, and performing tasks autonomously. In contrast, LLMs are designed for generating human-like text, answering questions, and processing natural language.

2. Autonomy: AI Agents can operate independently based on pre-set rules and AI capabilities, executing complex workflows without constant human intervention. LLMs, on the other hand, require user prompts to generate responses and do not act autonomously.

3. Integration: AI Agents interact with APIs, databases, and various tools to execute automation and process structured data. LLMs primarily handle text-based tasks and natural language processing without direct integration with external systems unless explicitly configured.

4. Use Cases: AI Agents are widely used for process automation, customer support, and AI-driven workflows where task execution is necessary. LLMs, however, are mainly used for tasks such as content generation, chatbots, and language translation where text-based intelligence is needed.

In essence, AI Agents function as intelligent orchestrators that can incorporate LLMs as one of their tools to process and generate human-like responses when needed while also handling automation, logic, and task execution.

AI Agents Can Learn from Prompts, Context, and History

A major advantage of AI Agents is their ability to learn from prompts, user context, and history, allowing them to be fine-tuned for specific tasks.

For example, if a user initially asks, “Tell me how many items I have in my inventory,” the AI agent might respond with:

In your inventory, you have the following items and their counts:
1. Milk: 4
2. Redbull: 4
3. Bread: 2
4. Butter: 5
5. Jam: 0
6. Chicken: 2
7. Protein Bar: 10

Total items: **27**

However, if the user refines their request by saying, “Can you share just the total number instead of the detailed list?”, the AI agent will adjust accordingly. The next time the same prompt is asked, the agent will respond with only:

Total items: **27**

This adaptability allows AI agents to be tuned for specific business needs and improves user experience by dynamically adjusting to requests.

How to Build a No-Code AI Agent with n8n.io

Scenario: Inventory Inquiry with Slack Integration

Our n8n.io workflow allows users to query their inventory via chat. The AI agent retrieves the input message, processes it using OpenAI’s chat model, reads data from Google Sheets and returns the corresponding response. The integration between the chat trigger, AI agent, and memory allows it to provide accurate, context-aware answers.

In this scenario, we build a no-code AI Agent in n8n.io that:

• Takes a user message in Slack as a trigger.

• Queries a Google Sheets document where inventory items and their counts are stored.

• Uses OpenAI’s Chat Model for language understanding.

• Uses Window Buffer Memory to remember past interactions.

• Sends a summary of the inventory back to Slack.

Simple overview of our AI Agent

The image showcases an n8n.io workflow where an AI Agent processes a user query and returns a response.

Input (Left Panel)

• Trigger: “When chat message received”

• The workflow is triggered when a chat message is received.

• The input data includes:

• sessionId: A unique identifier (c2a866bec451457f843978b78011d526)

• action: sendMessage (indicating the user sent a message)

• chatInput: "How many protein bars do I have?" (the user query)

Processing (Middle Panel - AI Agent)

• The AI Agent node is being used to process the user input.

• Agent Type: “Tools Agent”

• Source for Prompt (User Message): “Connected Chat Trigger Node” (this means the agent retrieves user input from the chat trigger node).

• Prompt (User Message): {{$json.chatInput}} (dynamically fetches the user’s message from the input).

• User Query: “How many protein bars do I have?”

• Require Specific Output Format: Disabled (the agent is not enforcing a strict response format).

• Chat Model & Memory: The AI agent is using a chat model and memory system to generate responses.

Output (Right Panel)

• The AI Agent processes the input and returns an answer.

• Output: "You have 10 protein bars."

• The agent has checked stored data and retrieved the count of protein bars.

• The response is clear, structured, and directly answers the user’s question.

Step-by-Step Guide to Connecting n8n.io with Google Sheets, ChatGPT, and Slack

Step 1: Connect Google Sheets to n8n.io

1. Add a Google Sheets Node in n8n:

   • Open your workflow in n8n.

   • Add a Google Sheets node.

2. Authenticate with Your Google Account:

   • Click on Credentials in the node settings.

   • Select Create New Credential and choose Google OAuth2.

   • Click Sign in with Google and grant access to n8n.

3. Connect a Specific Google Sheet:

   • Select the Drive where your sheet is stored.

   • Choose the Spreadsheet from the dropdown menu.

   • Select the Sheet you want to work with.

   • Test the connection to ensure it fetches the correct data.

My data in Google Sheet for this case

Step 2: Connect ChatGPT to n8n.io

1. Get OpenAI API Key:

   • Sign up or log in to OpenAI.

   • Navigate to API Keys under your account settings.

   • Click Create API Key, copy it, and store it securely.

2. Set Up OpenAI in n8n.io:

   • Add an OpenAI Chat Model node in n8n.

   • Select API Key authentication.

   • Paste your API key into the authentication field.

   • Test the connection by sending a sample request.

Step 3: Add Memory to the AI Agent

1. Add a Window Buffer Memory Node:

   • This helps the agent remember past interactions.

   • Configure it to store previous messages and responses.

   • The number inside is telling agent how many past conversations agent will remember.

2. Link the Memory to the Chat Model:

   • Connect the Memory Node to the OpenAI Chat Model so it can reference past exchanges.

Step 4: Add a Trigger Using n8n Chat

1. Add an n8n Chat Trigger Node:

   • This allows users to interact with the agent directly within n8n.

   • Configure it to listen for prompts.

2. Define User Inputs:

   • The fact is you don't need to define it in this case. You can just open chat and write any prompt. The AI Agent will take it.

3. Connect to OpenAI Model:

   • Send the user prompt from n8n’s chat to OpenAI for processing.

• As you can see - our agent is kinda advanced in thinking. When you ask just about "protein", our agent will include even chicken without telling him that chicken contains a lot of protein.

Step 5: Connect Slack to n8n.io

1. Create a Slack App (you need to be Slack workspace administrator):

   • Go to Slack API.

   • Click Create an App and select From Scratch.

   • Enable Incoming Webhooks and create a bot user.

2. Generate Slack Bot Token:

   • In OAuth & Permissions, add necessary scopes (e.g., chat:write, channels:read).

   • Click Install App to Workspace and copy the Bot User OAuth Token.

3. Set Up Slack in n8n.io:

   • Add a Slack Post Message node in n8n.

   • Paste the OAuth Token from Slack into the authentication field.

   • Specify the Slack channel where messages should be sent.

   • Test the connection by sending a sample message.

Step 6: Test it

• Test by sending a prompt via n8n chat and validating the response.

• And it's done

You can see my AI Agent connected via Slack App that I created is writing outputs to our Slack channel.

So what's the outcome?

AI Agents built using n8n.io offer endless possibilities for automating business operations. Whether you're looking to streamline inventory management, automate customer interactions, optimize workflows, or integrate multiple platforms, these intelligent agents can save time, reduce manual effort, and enhance efficiency.

By leveraging AI Agents, businesses can create intelligent, context-aware automation that learns and improves over time. The ability to integrate tools like Google Sheets, Slack, and OpenAI makes it even easier to build custom solutions without writing code. There is more like 400+ tools we can connect and use for our AI agents in n8n.io

The future of automation is here, and AI Agents are at the forefront. If you're looking to implement a powerful AI-driven workflow, now is the time to explore n8n.io and unlock new efficiencies.

🚀 Start automating today and revolutionize the way you work! 🎯