Why I Build My Own AI Agents: The Unexpected Advantages of Customization

When I first started working with AI agents, I didn’t realize how much the journey would mirror other creative challenges I’ve tackled before.

I’m the kind of person who tends to dive into the most complex aspects first and work my way through the details.This approach allows me to fully experience the build process, identify potential challenges, and ultimately share insights that can help educate others. This mindset shaped how I approached building multi-agent systems (MAS). Initially, I didn’t know about platforms like Microsoft Copilot, Azure, or n8n—I just started coding from scratch.

The "Aha" Moment: From Webflow to MAS

Before I ever thought about multi-agent systems, I was working with Webflow—a no-code platform for web design. Unlike traditional WYSIWYG (What You See Is What You Get) tools, Webflow gives you granular control over every element. Creating something as simple as a button meant deciding on the outline, spacing, and how it interacts with the entire design. It’s not just about placing an object; it’s about understanding how that change affects everything else—like a Rubik’s Cube, where one move shifts the whole configuration.

When I started building AI agents, I naturally brought that mindset with me. I didn’t just want to throw together scripts and hope they worked. I wanted each agent to be purposefully built, knowing that even the slightest changes could impact the entire system. I envisioned creating agents that did more than just automate tasks—they would be intentional, dynamic, and thoughtfully designed to adapt and evolve within the broader ecosystem.

Why Start From Scratch?

Honestly, I didn’t begin with the intention of building MAS from the ground up. I just started coding agents to see if I could make them work. I had no idea about the drag-and-drop agent-builder platforms at first. Once I did learn about them, I still felt more comfortable sticking with my custom approach. Why? Because starting from scratch meant I could shape the agents to fit my vision without being limited by a platform’s user interface or structure.

One major realization came when I noticed how many no-code platforms require you to learn their interface. In many cases, the platform itself becomes the learning curve.

When you build agents from the ground up, you’re not limited by someone else’s design choices. You have the freedom to make your agents integrate seamlessly with your environment—whether it’s a Google Sheet, a web interface, or any other familiar space. Essentially, you’re embedding the intelligence in a way that adapts the agent to your team’s needs or your individual workflow, rather than bending your process to fit the tool.

Unexpected Advantages: More Than Just Automation

When I first created my document retrieval agent, I thought it would simply automate gathering documents from online archives. But as I developed it, I started seeing the potential for it to be more than just a simple automation tool. I didn’t just want something that retrieved documents and stopped there. I wanted it to think, adapt, communicate, and collaborate with other agents outside my ecosystem. That’s when I pushed past just making a functional agent—I wanted it to be as close to living and breathing as possible.

The big surprise came when I saw how scalable and collaborative these custom agents could become. It wasn’t just about one agent doing one job. It was about making agents that could interact, collaborate, and evolve—almost like a small, intelligent team. Building them from scratch allowed me to embed switches and triggers that can be toggled on or off depending on the situation—whether they needed to pull data in real-time or switch to a more passive monitoring role.

The Freedom of Custom-Built Agents

One of the biggest advantages of building my own agents is that they are mine to control and modify. I don’t have to worry about subscription fees or losing access if a platform changes its pricing. Plus, because I own the code, I can contract out the agents, license them, or even loan them to others as if they were members of a flexible, digital workforce. How amazing is that!

Another crucial point is ownership of data. With drag-and-drop platforms, you’re often at the mercy of how they store, process, and secure your data. Building agents from scratch allows me to integrate secure data storage or build in custom compliance checks.

Through this process, I learned about the importance of following standards like those from the IEEE (Institute of Electrical and Electronics Engineers) and FIPA (Foundation for Intelligent Physical Agents). These standards aren’t just for large-scale corporate projects—they’re for anyone who wants to ensure their agents communicate efficiently and work well together. Implementing standards like FIPA-ACL for communication makes it easier for me to expand my system and integrate new agents without starting from scratch each time.

Why I Encourage Building Your Own

If you’re on the fence about building custom agents versus using platforms, I’d recommend going for the custom route if you can. Alternatively, start exploring custom options while using drag-and-drop platforms. Learning to build custom agents gives you:

1. Complete Control: Build agents that work exactly how you want them to.

2. Cost Efficiency: Avoid subscription fees, reduce API call expenses and other costs.

3. Flexibility and Customization: Integrate your agents into your existing workflow, whether that’s through spreadsheets, web apps, or other systems.

4. Ownership and Longevity: Your code is yours forever, and you can scale or adapt without being locked into a platform.

5. Contracting Opportunities: You can license or loan your agents, treating them as your own digital workforce.

My journey building agents has been full of challenges and learning. However, the journey has shown me just how powerful it is to start from scratch and build something truly unique.