AI agents are becoming one of the most valuable technologies for modern businesses. They automate repetitive tasks, understand company data, interact with your tools, and save teams hours every single week. As more companies adopt AI agents, one question keeps coming back: How do you actually build an AI agent?

Tldr : Today, there are three main ways to do it:

1. Using automation platforms (Zapier, n8n, Gumloop)

2. Using a no-code AI agent platform (like Calk AI)

3. Coding your own custom AI agent from scratch

Each method leads to very different results in terms of complexity, control, speed, and value.
This article walks you through all three, in simple words, so you can choose the one that fits your business.

If you want to learn what is an AI agent, you can read this article : Read it

1. Building an AI Agent With Automation Tools (Zapier, n8n, Make, Gumloop)

This is the “classic” way of building automations, and it has now evolved to include AI steps.
Think of it like building a flowchart: one trigger starts the process, then each block runs a specific action.

With tools like Zapier or n8n, you design a sequence:
a new lead arrives → the AI reads it → extracts insights → checks another tool → updates a file → sends a message.

You're not creating a human-like agent — you're creating a workflow agent, a chain of actions that use AI to make decisions along the way.

This method has a lot of power because you can combine dozens of tools and API calls. You can instruct the AI model to classify information, write a message, or decide what the next step should be. But every step must be configured manually, and you need to understand automation logic, data formats, and how to debug flows when something breaks.

The outcome is excellent if you want highly structured, multi-step technical processes. However, it requires time and technical understanding. It can also break very easily and to fix it, it can be complex when you don't have a technical or a tech-savy background. For many companies, maintaining workflows becomes harder as the business evolves. It’s a great choice for operations teams, technical founders, and people already familiar with automation tools — but for non-technical users, it can feel overwhelming.

If you want to learn how to do so, this video is a step by step approach on how to build one :

2. Building an AI Agent Using a No-Code AI Agent Platform (Calk AI)

This is the simplest and most accessible way to build an AI agent today.
Instead of designing workflows or writing code, you simply create the agent’s identity, connect your data, and let the platform handle everything else.

A no-code AI agent platform like Calk AI allows you to create agents that behave like digital coworkers. They can read your documents, understand your internal knowledge, connect to your tools (HubSpot, Google Sheets, Notion…), and perform tasks using real company context.

Building an agent becomes as easy as:

1. Selecting the type of agent you want (sales, marketing, support, operations, etc.)

2. Writing a clear description of its role and behavior

3. Connecting the documents, systems, and apps it needs

4. Asking it to run tasks immediately

There are no flowcharts, no triggers to configure, no APIs to manage.
The platform handles the intelligence, the reasoning, and the connections.

The big advantage is speed: anyone in your company can build an agent in minutes, not in days or weeks. It’s perfect for teams that want productivity and real results quickly.
The limitation, of course, is that you’re not building complex multi-step automations; the goal isn’t deep technical orchestration — it’s practicality, accessibility, and real business impact.

If your priority is efficiency, speed, and giving your team powerful assistants connected to your tools and data, this method is by far the best.

In the video below, you will discover how to build an AI agent in a few clicks :

3. Building an AI Agent by Coding It Yourself

This method gives you the most flexibility, but it’s also the most complex by far.
Developers can build fully custom AI agents using languages like Python or JavaScript, combined with frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, or the raw OpenAI/Anthropic APIs.

When you build an agent with code, you control everything:
the reasoning loop, the tools, the memory, the integrations, the logic, the security, and the performance.
You can build custom actions, private knowledge retrieval systems, long-term memory modules, advanced tools, and even multi-agent systems that talk to each other.

But this freedom has a cost.
You need engineers, infrastructure, a vector database, API management, error handling, monitoring, and deployments. A custom-coded agent can take weeks or months to build, and maintaining it becomes an ongoing commitment.

Companies usually choose this method when they’re building AI products or when they need extremely specific behaviors that generic platforms can’t support. If you're a typical business trying to improve productivity, this method is often far more than you need.

Understanding the Differences

Even though all three methods let you “build an AI agent,” the experience and the results are completely different. Think of them like three types of assistants, each with its own personality and strengths.

Automation tools (like Zapier/n8n) are like building a robot that follows a strict to-do list.
You tell it: “First do this, then that, and if X happens then do Y.”
It’s powerful, but you need to think like a programmer.

No-code AI agent platforms (like Calk AI) give you a digital coworker.
You describe what you want, connect your data, and the agent understands your company.
It’s the easiest and fastest way to get a real assistant working alongside your team.

Coding your own agent is like building an employee from scratch — you decide how the brain works, what tools it uses, and how it behaves.
It’s extremely customizable… but also the most complex and time-consuming.

At the end of the day, the “best” method depends on your team’s skills and what you need:

• If you want something simple and fast, choose a no-code platform.

• If you need multi-step automations, go with Zapier/n8n.

• If you want full engineering control, coding is the right path.

The goal isn’t perfection — it’s building an AI agent that actually saves you time and performs tasks your team shouldn’t waste hours doing manually.

Tdlr ? Read the conclusion

Building an AI agent in 2025 is no longer a technical dream reserved for engineers. Whether you prefer automation workflows, a no-code AI agent platform, or full custom coding, the tools now exist to create agents that genuinely help your business work faster, smoother, and with far less manual effort.

If your goal is to automate structured, multi-step processes, automation tools like Zapier and n8n are a solid choice. If you're technical and need complete control over every detail, coding your own agent gives you unlimited flexibility — though it requires a serious investment of time and expertise.

But for most businesses, the simplest and most impactful option is a no-code agent platform. It lets anyone create a digital coworker connected to real company data, without building workflows or touching code. It’s the fastest path from idea to productivity.

Whichever method you choose, the point is this:
AI agents are becoming a core part of modern operations. The sooner your company starts building them, the sooner you unlock time, efficiency, and a competitive advantage.