How to Build an AI Agent Without Being an AI Expert

Building and training your own AI agent has mostly been a figment of imagination or something seen in sci-fi movies. Today, it’s a reality accessible to anyone with a computer and an idea. While artificial intelligence may sound intimidating, the recent rapid development of AI technologies has made it possible to build an AI agent, train it, and deploy it. The world has already seen rapid adoption of this AI trend, and the market growth shows promising numbers. Recent reports show that the global AI agents market size reached USD 5.40 billion at the end of 2024 and is expected to grow at a 45.8% CAGR through 2025-2023.

This guide will explore AI agent development, the necessary steps of creating an AI agent, and mistakes that you should avoid. We hope to demystify artificial intelligence technologies, how they work, and, most importantly, how you can build one to solve your business challenges.

What is an AI Agent?

Before we discuss how to build an AI agent, we need to understand what they are. An AI agent is a software program, a smart assistant, that can understand information, make decisions, and take action without human supervision and constant prompt input. These agents listen to what’s happening around them, think about the best responses, act by answering questions or completing tasks, and, over time, learn from their interactions to improve their accuracy and usefulness.

AI agents can be anything from chatbots that handle customer service to AI sales assistants and automated document processors.

There are four main types of AI agents:

• Simple reflex agents: They have sets of rules and actions and don’t hold memory or interact with other agents to fill in the missing information. This agent could be a simple chatbot that answers FAQs on your website.
• Model-based reflex agents: These agents respond to current situations, remember past interactions, and maintain an internal model of the world to make informed decisions. For example, a thermostat that adjusts the temperature based on current conditions and the users’ habits (historical data).
• Goal-based agents: When creating an AI agent like this, you define specific goals and program a set of actions. Goal-based AI agents search for particular action sequences, plan them, and act on them to achieve their goals. For example, an AI scheduling assistant helps you find the most suitable meeting period.
• Utility-based agents: These agents evaluate different actions, consider various factors and potential outcomes, and only take action after deciding on the path that maximizes users’ overall satisfaction. For example, a delivery robot that takes the most optimal route considering speed, energy consumption, etc.
• Learning agents: These agents have the same capabilities as other agents but can also learn and improve. They analyze new data and experiences autonomously, learn from them, and are well-equipped to operate in unfamiliar environments. For example, an online shopping platform that suggests products based on your past purchases.

How to Build an AI Agent Step-by-Step

It’s time to answer the question, “How do I build an AI agent?” There’s no simple answer, but the process is quite straightforward, involves several essential steps, and requires a lot of testing and validation.

You begin AI agent development by identifying the problem your agent will solve for your users. It’s an essential and unskippable part of creating an AI agent that affects and dictates the success of your product.

First, you need to define the goals. Are you looking to improve customer satisfaction, improve internal processes, or provide your users with personalized recommendations? The goals must be measurable and precise to help successful AI agent development. Once you define the purpose of your AI agent, you must choose the type of Agent you want to build.

Choosing the right platform for your AI agent development will determine its performance, scalability, and overall effectiveness. Several options are available, including no-code, low-code, and custom platforms.

• No-code platforms use drag-and-drop components to allow users to build AI agents without coding knowledge
• By creating an AI agent on low-code platforms, you can still easily build the program and include minimal coding for customization
• Custom AI agent development involves a professional development company that offers customization, scalability, and support
• Open-source platforms offer publicly accessible codebases that you can use and later modify

While no-code and low-code platforms offer a certain level of comfort for non-technical folks, they can’t promise customization and scalability.
Open-source platforms solve this issue and allow you to customize your AI agent. However, they require programming skills and don’t include other necessary steps for creating an AI agent.

Working with a custom AI agent development company can be more beneficial since their services usually include everything from planning to deployment and testing.

So, how to build an AI agent? Which platform to choose? Here’s a little visual aid to help you compare these platforms:

As you can see, custom development offers the most tailored, scalable, and integrated solutions, which makes it the best option for companies looking to understand how to build an AI agent that aligns with their unique needs.

The AI agent development process includes eventually training the agent to perform the desired tasks without human supervision. Before training, you must collect, polish, and input data.

Collect and prepare training data

AI agents learn from the data that you feed them. If your data is error-ridden, the agent will make mistakes; if the quality of your data is poor, it may perform incorrectly, requiring constant updates and adjustments.

Once you’ve collected the data you want to feed your AI agent, it’s time to prepare it:

• Remove duplicates to feed your AI agent unique data and avoid skewing results
• Address inconsistencies and standardize formats (dates, currencies)
• Fill in the missing data and identify informational gaps
• Label and categorize data for easy retrieval and organization

Train your AI agent

Once you have your data prepared, it’s time to train the AI agent. Here, you teach your agent using examples you’ve provided.

Here’s what you need to train your AI agent:

• Choose the training method (supervised, unsupervised, reinforcement learning)
• Feed your model the data you’ve collected
• Fine-tune training parameters like learning rate, batch size, and the number of epochs
• Monitor the training process to make necessary adjustments
• Test your AI agent with a separate dataset

By following these steps and understanding how to build an AI agent effectively, you can create a well-trained, customized agent capable of delivering valuable interactions and solutions to users.

This step involves running various tests to ensure the accuracy and efficiency of your AI agent. Quality assurance engineers play a crucial role in this process.

These are the key components of testing your AI agent:

• Predefined tasks: These scenarios mimic real-world interactions your users might have with the AI agent.
• Performance metrics: During this phase, it’s essential to measure your agent’s accuracy, keeping in mind that there might be some overfitting or underperformance.

In case your QA engineers find some issues or errors, or your AI agent isn’t performing up to expectations, you’ll need to revisit the training phase, adjust some parameters, and start testing again.

Here’s how you address overfitting and underperformance:

• Adjust parameters and tune hyperparameters
• Retrain the model by providing additional and more diverse training data
• Continue testing and adjusting

Once you’ve done testing and seen your AI agent’s performance in simulated scenarios, it’s time to see it in a live environment.

How to prepare for deployment:

• Set up the infrastructure required to host your AI agent (cloud services, on-premise solutions)
• Start integrating your AI agent with existing systems (CRM, ERP)

Once you’ve prepared your agent for the deployment, it’s ready to roll out your new software:

• Staged rollout: Start with a small group of users to gather feedback before a full-scale launch
• Monitoring tools: Implement monitoring tools to analyze user interactions

Over time, the demand for your agent will increase, and you’ll notice the necessity of adding new features or functionalities. This is where customized AI agent development comes in handy – it allows you to expand the agent and customize it further.

How Can Glorium Technologies Help?

While building an AI agent is a complex process that requires careful planning, the right partner choice can make it easier. Whether you’re starting from an idea or want to improve an existing AI agent, Glorium Technologies can help you with comprehensive AI agent development services. If you’re wondering how to build an AI agent that truly aligns with your business goals, schedule a call with our AI experts to discuss your idea.

AboutVolodymyr Khomichenko

Marketing Director at Glorium Technologies