How to Build an AI Agent: Step-by-Step With No-Code or Code

Features and pricing in this article were checked on each company's official pages on October 2, 2026. Features and plans can change. Before use, check the latest information through the sources at the end.

An AI agent is a setup in which an AI model makes its own decisions and uses tools to get work done. Anthropic's article distinguishes workflows, which connect AI models and tools through predefined steps, from agents, in which the AI model decides its own steps and how to use tools. Both can be built with the methods in this article.

Three things to decide first: the job, the input and the output

Whichever method you choose, decide three things before you open a tool. If these are not settled, the goal shifts as you build, and you cannot judge whether each test result is good or bad.

• The job: narrow it to one task. Check that you can describe success in one sentence
• Input: what triggers it and what data it receives, such as email, forms or spreadsheet rows
• Output: what it returns and where. Does a person check it before it goes out, or is it sent as is?

Anthropic's article recommends looking for the simplest approach first and adding complexity only when needed. If the job can be done with fixed steps, a workflow may be enough, without making it an agent that decides on its own.

Building with no-code: n8n and Dify

The leading no-code options are n8n and Dify. In both, you build by connecting components on screen. It is an easy way to start, even for non-engineers.

The flow in n8n

1. Add a trigger node, such as a chat message, a schedule or a webhook
2. Add an AI Agent node
3. Connect a chat model to the AI Agent node
4. Connect at least one tool node. The AI Agent node needs one or more tools
5. Connect a node that sends the result, then run it manually to check

According to n8n's documentation, the AI Agent node chooses and calls the connected tools that fit the task. n8n also has a feature for building standalone agents separate from workflows. As of October 2026, this feature is in preview. Detailed steps are in Building an AI agent with n8n.

The flow in Dify

In Dify, you can create an app of the Agent type. It works in a chat format: the AI model thinks, decides what to do next and uses tools when needed. Conversation history is kept up to 500 messages or 2,000 tokens per conversation.

Dify's documentation lists these five things to write in an Agent prompt.

• Role: what position it takes and what expertise it uses
• Output format: structure, length and style
• Constraints: what to avoid and which rules to follow
• Tool usage: which tools to use in which situations
• Process: the order in which to work through complex tasks

You can also add variables to the prompt. When the user enters values before the conversation, they are inserted into the prompt. This is handy for reusing the same agent across different fields or targets.

You can also place an Agent node inside a Workflow. Use this when you want the agent to handle just one step within a fixed sequence. Dify also has a new Agent that runs commands and reads and writes files in its own sandbox. As of October 2026, it is in beta.

Building with code: SDKs and frameworks

To build with code, use an SDK from the AI model provider or a framework for agents. As an example, here is the flow for the OpenAI Agents SDK.

1. In a Python environment, run pip install openai-agents to install it
2. Set your API key in the OPENAI_API_KEY environment variable
3. Give the Agent a name and instructions
4. Pass Python functions as tools
5. Run it with Runner and check the result

The official documentation explains that the SDK keeps its core building blocks to three: Agents with instructions and tools, Handoffs that pass work to other agents (or the pattern of using agents as tools), and Guardrails that validate inputs and outputs. It also includes Tracing so you can follow what happens.

The same documentation also notes when calling the Responses API directly, without the SDK, is better: when you want to manage the loop and tool calls yourself, or when a short process only needs to return a model response. The SDK suits cases where you want it to handle conversation management, tool execution and guardrails.

Anthropic's article points out that frameworks make it easy to start but can hide the underlying prompts and responses, which makes debugging harder. It recommends using the AI model API directly first and, if you use a framework, understanding the code underneath. How to compare frameworks is covered in AI agent frameworks.

Choosing between no-code and code

If you are unsure, you can build the first version with no-code and move to code once you hit its limits. The three tools are compared in n8n vs. Dify vs. GPTs.

Ways to try it at no cost

While you are learning on your own, there are ways to try building at no cost. This is official information as of October 2026.

• n8n Cloud: a 14-day trial lets you try Pro features
• Self-hosted n8n: the Community edition has no license fee. You prepare and manage the server yourself
• Dify Cloud: the Sandbox plan has no fee. Up to 5 apps. It comes with 200 message credits for trying AI models, which do not renew monthly
• Self-hosted Dify: the Community Edition is listed on the pricing page as a version with no fee. If you use it for something you sell, check the license terms

Watch out for AI model usage fees. When you call a model with your own API key, the model provider bills you separately. While testing, use short inputs and set a limit on the number of runs.

How to test and fix

AI agents give slightly different results even with the same input. Do not stop after one success. Try the same input several times.

• n8n: pinning a node's output lets you test without calling external services again and again. Pinned data is not used in production runs
• n8n: you can load the data from a failed execution into the editor to fix it
• Dify: you can run a single node and check its input, output, time and errors
• Dify: per-run history lets you trace which nodes ran in what order

Anthropic's article explains that because agents act autonomously, costs go up and errors can compound. It therefore recommends extensive testing in sandboxed environments and appropriate guardrails. Turn off irreversible actions, such as sending email or making payments, while testing.

The same article gives three principles for building agents: keep the design simple, make the agent's planning steps visible, and carefully craft tool documentation and tests. According to the article, most agents are simply systems that use tools in a loop based on feedback. That is why it considers clear tools and tool descriptions essential. When you fix something, review the tool descriptions as well as the prompt.

Getting people to use the AI you built

People can use your AI agent only after you publish it. In n8n, publishing a workflow activates its production webhook URL or schedule. In Dify, you can publish as a web app, an API or an embed on a website.

When others use it, be ready to explain what it can handle, what goes in and what comes out, which accounts or API keys are needed, and who pays for them.

Employee Store is a marketplace where companies can adopt AI agents built by developers, with a one-time purchase or a monthly plan. Any format works. You can list n8n or Dify workflows, custom agents built on the Claude or OpenAI API, and more. There is no listing fee and no upfront cost. The commission is 20% of the transaction amount and applies only when a sale closes. See the seller guide for details.