Deploying AI Agents in n8n Workflows

A groundbreaking research paper from Google

In 2017, Google published a groundbreaking research paper “Attention Is All You Need” introducing the Transformer architecture. That moment sparked a wave of innovation.
Since then, companies around the world have been experimenting with Large Language Models (LLMs).

But everything changed with the launch of ChatGPT, the turning point that brought AI into everyday conversations. From that moment, a new revolution began. Now we are heading to era of “Agentic AI”.

Today, if you look at any major tech company - Amazon, Meta, Microsoft. They all have one thing in common: AI is their top priority. This isn’t just hype. It’s the new foundation of how products are built, how decisions are made, and how companies scale. I think in near future, AI won’t just be your co-pilot. it will be the pilot. If you are a solo founder or a team with limited resources, you must have a solid grasp on how to leverage AI. That is the reason I will discuss which tool to start automations with it & how to identify when to shift tools based on the task allocated.

Differences Between LLMs, AI Agents, and Agentic AI

Large Language Models (LLMs): These are advanced neural networks trained on huge text datasets using the Transformer architecture. They break down text into tokens (small chunks like words or characters) to process and understand language. By analyzing billions of token patterns, they learn context, grammar, and meaning. When you provide a prompt, they predict the next word and generate the most likely sequence of tokens in response.
LLMs can process and make decisions, but without tools, they can't perform real-world actions. That's where AI Agents come into play.

AI Agents : Think of AI Agents as LLMs augmented with tools. As previously discussed, LLMs can understand instructions and generate responses, they lack the ability to take real-world actions. AI Agents bridge this gap by integrating tool access, such as APIs, browsers, or databases & enabling the model to execute tasks beyond text generation. For instance, if an agent is granted access to Gmail and Google Sheets, it can interpret your instruction (“Email X and log it in the sheet”) and autonomously interact with those services to complete the task.

Now what is Agentic AI ?

AI Agents & Agentic Ais are alternatively used in general conversations but there exists a difference a subtle difference between them. Agentic AIs represent a more advanced orchestration of AI Agents.

While AI Agents are typically single-task systems that combine an LLM with a set of tools (e.g., APIs, web access, databases) to execute linear, goal-driven instructions, they are suitable for performing one task in isolation at a time based on user prompts.
In contrast, Agentic AI introduces multi-agent orchestration, where multiple specialized agents each with domain-specific tools and responsibilities collaborate autonomously.

Think of it as building a distributed system of agents, analogous to departments within an organization (e.g., Sales Agent, Support Agent, Ops Agent), where each agent communicates, delegates, and adapts based on shared goals.

This requires a sophisticated runtime framework that supports:

• Dynamic role assignment

• Memory management and state persistence

• Planning, reflection, and decision loops

• Asynchronous task coordination and feedback integration

In essence, Agentic AI is about enabling a collective of intelligent agents to work together autonomously.

What is n8n & Why start with n8n ?

The automation ecosystem can be broadly divided into three categories based on the level of coding required: no-code, low/mid-code, and full-code platforms.

No-code automation tools like Zapier, make (Integromat), Pabbly, and many are designed for business users with little to no programming experience. These platforms provide visual interfaces to connect services and automate workflows using drag-and-drop logic. While they are quick to deploy and great for basic automations, they offer limited flexibility and customization for advanced use cases.

Mid-code platforms, such as n8n, strike a balance between visual simplicity and programmatic flexibility. n8n allows you to build powerful, multistep workflows using a visual node-based editor without writing a single line of code. However, for users with technical knowledge, it also offers the ability to inject JavaScript/Python code in function nodes, handle error workflows, and integrate with APIs via HTTP requests. This hybrid model gives you the best of both worlds: ease of use for rapid prototyping and the option for deep customization when needed.

On the other end of the spectrum, full-code automation is powered by programming languages, most commonly Python. With Python, you have full control over every parameter. You can build intelligent agents from scratch, write custom modules, and integrate any service using libraries and APIs. However, this approach is time intensive. Libraries like LangChain help modularize LLM-based workflows, while LangGraph (built on top of LangChain) enables building non-linear, agentic execution graphs for complex reasoning tasks.

This is where n8n becomes strategically very powerful. It sits between traditional AI Agents and full Agentic AI systems giving you a scalable, modular, and visual platform to start building intelligent workflows today, while also preparing you for more complex, agent-based orchestration in the future. By starting with n8n, you gain practical exposure to how automation works under the hood.

In short, n8n is not just a tool, it’s a gateway for setting up agentic systems.

Architecture of n8n

• Workspace : The main canvas to design your workflow.

• Nodes : Tasks or actions you add to build the workflow.

• Trigger Event : The starting point that activates the automation

Build Your First AI Agent

Click the “+” button on the top right (Shortcut: Tab) to add your first node.

• Trigger Node : Use “Chat Message Received” as the entry point to initiate your workflow based on incoming user input.

• AI Agent Node : Connect the trigger to the AI Agent node. In the user prompt, pass the dynamic input. In options, configure the system message to define your agent's behaviour or instructions.

Now, your agent has three fundamental components:

• Model : The LLM (e.g., OpenAI GPT) acting as the cognitive engine.

• Memory : Manages conversational context by storing previous interactions.

• Tools : External services the agent can access (like Gmail, Sheets, APIs).

Next, configure the OpenAI Chat model node by authenticating with your OpenAI API key. Refer to OpenAI Credentials Setup in n8n for detailed guidance. For memory handling, n8n provides basic conversational memory via the context length setting, defining how many past messages the model should retain for continuity.

Add the Gmail Node to empower your agent with email-sending and reading capabilities. Set up Gmail credentials by following: Google Auth Setup in n8n. The node supports operations like to send, get, reply, and more, documented here: Gmail Node Capabilities.

If you have done all the steps above & it’s successful. Then congrats, you've done it !