How to Build an AI Agent: A Step-by-Step Guide for Businesses

If AI agents feel like they’re suddenly everywhere, it’s because they’re meeting the moment. In one form or another, intelligent agents are making waves in how businesses operate today. They take over repetitive tasks, can process mountains of data, and are autonomous enough to make decisions on their own. Simply put, an AI agent fulfills every need of the companies that seek to gain better efficiency. If you, too, have been googling how to build an AI agent without a Silicon Valley-sized budget, you’ve come to the right place.

As an AI software development company, we see a heightened interest in AI agents because these are chat apps with richer functionality and help users in more ways. Given nearly 97% of business owners think ChatGPT can help their company in one way or another, it is no wonder AI agents are hits. Which leads us to ask—how do you build one? With the right tools, expert insights, and a little guidance (hint: this guide), you’ll be able to design an AI agent for your needs.

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What is an AI agent?

Put simply, an AI agent is a program or system designed to act autonomously or semi-autonomously to achieve certain goals. Think of it as a problem-solver, decision-maker, or even a task-completer that operates with minimal human intervention. 

AI agents power some of the well-known and popular tools individual users and businesses use today. Virtual assistants like Siri or Alexa, chatbots that handle customer queries, autonomous vehicles that navigate traffic, and even intelligent systems optimizing supply chains—all of these are AI agent examples in action.

How AI agents work

To be able to perform tasks autonomously, AI agents use artificial intelligence and operate in a continuous cycle, which involves the following sequence of activities:

• Perception. The agent gathers information through sensors or inputs, depending on the solution type (a chatbot that analyzes text or a drone that scans its surroundings).
• Reasoning. Using the gathered data, the agent evaluates the situation and decides the next step.
• Action. It executes the decision, such as sending a response, adjusting a system, or moving to a location.
• Learning. The agent improves its performance by analyzing past decisions, outcomes, and user feedback, often leveraging machine learning.

For instance, a customer service chatbot starts by analyzing a user’s input (perception), identifies the intent behind the query (reasoning), delivers the most relevant response (action), and logs the interaction to refine its future responses (learning). Similarly, an autonomous drone scans its surroundings, calculates the fastest path forward, and adjusts its flight route. And with every new piece of data, its navigation system is improved.

Types of AI agents

Now that we have clarified what is an AI agent and how it works, let’s see what kinds of smart agents exist. We can group AI agents based on their level of intelligence, decision-making capabilities, and the types of AI models they use:   

• Reactive Agents respond only to what’s happening now and can’t take into account the past or plan for the future, e.g., a thermostat adjusting temperature based on immediate feedback.
• Deliberative Agents, on the contrary, plan their actions using a model of the world and their goals. They can reason about the future but may be slow in complex environments. Some of the examples include autonomous robots that map unfamiliar terrains, logistics systems, financial forecasting models, and others.
• Hybrid Agents combine reactive speed with deliberative thoughtfulness. Virtual assistants, for instance, often fall into this category, quickly responding to requests while considering user preferences.

Why build an AI agent?

Let’s be honest: the world won’t slow down, and businesses need new, more efficient ways to keep up with the pace. AI-driven chatbots are already highly effective tools for automating a number of tasks, with 73% of companies using them for instant messaging. So, AI agents are the next logical step in this evolution that promises to take over even more tasks from your team. 

Practical applications

Believe it or not, every industry can benefit from AI agent development because its applications are incredibly versatile and adaptable. We’ll review just some of them to give you an idea of how far-reaching AI agents can be and how you can use them.

Healthcare

Although healthcare isn’t considered the leader in adopting new technology, it finally rethinks its decades-old practices and badly organized approaches to care delivery. The industry is actively catching up by integrating AI agents, among other technologies, to streamline operations and improve patient outcomes. AI implementation in healthcare helps doctors catch diseases earlier and with greater accuracy. Smart agents take over routine tasks like patient records management and scheduling, as well as assist in surgeries, acting as a second pair of “intelligent” eyes, which reduces risks and improves outcomes.

Finance 

Financial professionals build AI agents to be able to cope with huge masses of diverse data and spot patterns or trends that human specialists are physically unable to detect. For example, you need to analyze millions of stock market transactions from the last two years to understand how specific geopolitical events impacted trading volumes or market volatility. An AI agent can do it in a matter of moments, whereas identifying correlations and trends would take a human team months to uncover. That’s why they can also offer personal investment advice to you and your clients.

Customer Service 

Chatbots powered by AI provide instant, 24/7 support that can not only answer simple questions but also tailor responses to each customer. They don’t get tired, they don’t take breaks, and they leave your human agents free to deal with more complex or business-critical issues. 

Logistics 

AI agents applied in logistics help companies smoother operations with smarter route planning and inventory management. They can predict delays and offer solutions to fix them, optimize delivery schedules, or suggest the most cost-effective options for your supply chain challenges. 

Retail 

Personalized shopping experiences that don’t leave your customers indifferent are a hallmark of AI agents. They analyze what customers buy, view, or save to favorite and predict what they might need next. Plus, they help businesses send the right promotions to the right people at the right time, turning marketing into precise science, not guesswork. On top of that, with smart agents, your shelves will always stay stocked.

Benefits for businesses

Clearly, the most interesting part for the company decision-makers aiming to incorporate AI agents is the advantages they will bring. These tools can benefit organizations in the following ways:

• Automate repetitive tasks 

Think about all the tedious, manual work that eats up hours—data entry, appointment scheduling, routine email responses, and similar. Your team can rely on AI agents to handle such tasks easily and free your employees’ time for high-value tasks that require creativity and critical thinking.

• Improve decision-making 

AI agents process enormous amounts of data in seconds and present insights that make decision-making easier and smarter. It will work for predicting sales trends, understanding customer behavior, or finding inefficiencies in your operations.

• Cut costs 

Yes, building an AI agent requires an upfront investment, but the payoff is undeniable. Slightly over 30% of business leaders see lower labor costs thanks to automated processes, which also increase productivity and reduce errors.

• Enhance customer experiences 

Instant 24/7 support combined with personalized recommendations will make your clients satisfied and feel valued. Unlike simple chatbots, AI agents make interactions more meaningful, anticipate customer needs, and can resolve complex queries without human overview.

How to build an AI agent: Key steps 

Building an AI agent might seem daunting at first, but when you lay it out in easy-to-follow steps, it becomes much more manageable. If you’re curious about how to build an AI agent or how AI in software development can impact your business, we’ve prepared a practical guide to help you get started.

Step 1: Define the purpose and scope

Before you can move to the technical aspect, you should get crystal clear on why you need to build an AI agent. What problem are you trying to solve with the help of it? Is it to improve customer service or remove inefficiencies from your workflows? This ‘why’ is what will help you create a solution that truly adds value, not for the sake of jumping on the AI bandwagon. 

Define the specific goals and functionalities your agent should have. For instance, should it answer customer queries to improve response times, automate scheduling to free up employee hours, or be able to learn user preferences over time to deliver personalized experiences? Knowing these specifics helps you concentrate on what matters and avoid building one-size-fits-all AI agents that don’t fit anyone.

Step 2: Choose the right architecture

Here’s where things get a little technical, but stay with us. The architecture is essentially the “brain” of your AI agent. There are several options to select from:

• Rule-based agents that follow predefined rules are enough for simple, straightforward tasks. Think of a chatbot that answers FAQs or a thermostat that alters the temperature depending on preset thresholds.
• Machine learning-based agents are more autonomous and smart AI agents thanks to the ability to learn from data and improve their performance with time. Such tools can execute more complex tasks (give personalized recommendations and predict user behavior).

You’ll also need to decide between a simple agent, which works independently, or a multi-agent system, where multiple agents collaborate to solve larger problems. For instance, in logistics, one agent might handle route optimization while another oversees inventory.

Step 3: Collect and prepare data

Data is the lifeblood of all AI agents, rule-based and machine learning-based. Without high-quality data, your agent won’t perform as well as you want—no matter how good your algorithms are. So, you should identify the data you’ll need. For a customer service bot, this might be past chat logs. For a recommendation engine, it could be user behavior data.

Once you have your data, it’s time to clean it up. Remove duplicates, fill in missing values, and standardize formats. A well-prepared and cleaned dataset is non-negotiable if you want your AI agent to be accurate and reliable.

Step 4: Choose the right tools and frameworks

The tools and AI agent frameworks you pick can make or break your project. They shape how smoothly the development process goes and how well your AI agent performs in the real world. 

Here are some good options to consider:

• TensorFlow and PyTorch. These are the preferred tools for AI agent development. TensorFlow is great if you’re looking for something robust and production-ready, while PyTorch is perfect for experimenting and quick iterations. Both are good choices if you want to build and train machine learning models that can handle everything from image recognition to predictive analytics.
• OpenAI API. If you’re eyeing natural language processing or text-based tasks, this is a fantastic shortcut. It lets you tap into pre-trained models like GPT, so you don’t have to start from scratch. Want your AI agent to chat like a pro? This is your go-to.
• Dialogflow and Rasa. These are all about building conversational AI. Dialogflow simplifies the process of building conversational interfaces and makes it easy to integrate with Google Assistant, while Rasa gives you full control over the AI logic with its open-source framework. So, if you want to create a chatbot or voice assistant, these tools are what you need.

Now, let’s talk about languages. Python is the MVP here—it’s simple, versatile, and backed by an incredible number of libraries for AI and data science. If you need your AI agent to play nice with web apps, JavaScript is worth considering for front-end work. And for projects that lean heavily on data analysis, R might be the better choice.

Choosing the right combination of tools and frameworks depends on your agent’s complexity and purpose. A straightforward AI agent doesn’t need the heavy artillery of TensorFlow, but a machine learning-based one probably does. The point is to pick what works for your project goals without overcomplicating things. The right framework and tools can save you time, energy, and a lot of frustration down the line.

Step 5: Develop the core logic

The development of the core logic is what truly defines how to create an AI agent that’s functional and intelligent. That’s when you’ll build the “thinking” part of your agent and enable it to process inputs, make decisions, and take action effectively.

Start with perception, where the agent processes data inputs. Whether it’s analyzing text, interpreting images, or processing voice commands, this is where your agent learns to make sense of the world. Then, you can move on to develop decision-making and learning algorithms. This is where you define how the agent evaluates situations and improves over time. Will it follow a strict set of rules, or will it learn dynamically from new data? Finally, you’ll need to code output actions, which could be a generation of text responses for your chatbot or triggering physical movements in a robotic arm.

Step 6: Train and test the AI agent

Once the core logic is in place, it’s time to train AI agents so they actually know what to do. This involves feeding agents with data and refining its algorithms through methods like:

• Supervised learning. You give it labeled data (like images paired with correct labels), so it learns what’s right.
• Unsupervised learning. Here, the agent discovers patterns in unlabeled data on its own—perfect for clustering or anomaly detection.
• Reinforcement learning. This method teaches the agent through rewards and penalties, which is ideal for tasks where trial and error lead to mastery, like teaching a robot to navigate a maze.

After training, thorough testing ensures your agent performs as expected. You should run AI agents through various scenarios to check for accuracy, consistency, and robustness. 

Step 7: Deploy and monitor the AI agent

Now, the exciting part—deployment! Depending on your use case, you should choose one of the platforms:

• Cloud-based solutions are scalable and allow AI agents to process large amounts of data.
• Edge devices are perfect for AI agents that require low latency (IoT devices or autonomous systems).
• Hybrid solutions combine the benefits of both environments, offering high responsiveness and data processing power.

After the deployment, the work isn’t done. To keep your AI agent performing at its best, you’ll need to monitor its performance and fine-tune its behavior. It should become a routine check-up to ensure your AI agent is healthy and continues to deliver value.

Tools and frameworks for building AI agents

We want to go into more detail about tools and frameworks used to develop solid AI agents because the stack you choose is really important for the success of the end outcomes.

AI development frameworks

Generally, frameworks provide pre-built components and libraries that streamline the development process, as you don’t need to build everything from scratch. Instead, you can concentrate on the unique aspects of their AI agent. While you might already know that TensorFlow and PyTorch are the giants in deep learning, here’s what else they offer:

• TensorFlow. Beyond its flexibility, TensorFlow is great for deploying machine learning models on multiple platforms, from mobile devices to servers. It also has TensorFlow Lite for edge devices and TensorFlow Extended (TFX) for production-grade pipelines. It helps you scale up your AI agents when needed.
• PyTorch. Known for being researcher-friendly, AI developers also prefer PyTorch in production thanks to its TorchServe feature, which simplifies model serving and deployment. Its dynamic computation graphs make debugging easier, which saves you headaches when tweaking your AI agent.
• Scikit-learn. This is your best bet for classical machine-learning tasks like regression, classification, and clustering. It’s lightweight, easy to use, and perfect for AI agents that don’t need deep learning but still demand intelligent decision-making.

Natural language processing (NLP) tools

For AI agents that need to understand or generate human language, the incorporation of NLP is obligatory. From our experience, OpenAI GPT and Hugging Face Transformers simplify the development of conversational agents or text-based systems and make them much more approachable.

• OpenAI GPT. While GPT models are famous for their natural language understanding and close to human generation capabilities, their versatility is what truly stands out. Whether you’re building a customer support agent, creating personalized recommendations, or even summarizing complex documents, GPT makes it easier to handle language-intensive tasks.
• Hugging Face Transformers. This open-source library provides pre-trained models for a wide range of NLP tasks, including text classification, translation, and sentiment analysis. The best part? It’s super easy to fine-tune these models for your specific needs. Hugging Face lets you give your AI agents a personal touch—no massive datasets or heavy lifting required.

Deployment and integration tools

Once your AI agent is built, you need the right environment to deploy and integrate it into your systems. And here are also several good variants we suggest you consider:

• AWS SageMaker. This tool takes the complexity out of deploying and managing machine learning models. Packed with model monitoring, automatic scaling, integrated debugging, and a number of other useful features, SageMaker is a reliable tool for maintaining AI agents in good shape over time. Plus, its managed services mean less time worrying about infrastructure and more time refining your agent.
• Microsoft Azure AI. Azure offers numerous and diverse tools with pre-trained AI services like Language Understanding (LUIS) and full-scale custom machine learning pipelines, among the most prominent ones. If you plan to integrate your AI agent into Microsoft’s ecosystem, such as Teams or Dynamics 365, you won’t find a better variant. 
• Google AI Platform: Built for scalability, Google’s AI Platform lets you train, deploy, and monitor models in the cloud. It’s a good option if your AI agent depends heavily on data to make decisions, as it integrates seamlessly with BigQuery data warehouse for powerful analytics.

How to build an AI Agent: Best practices

No matter you’re going to create AI agents in-house or considering AI outsourcing, knowing how to do it properly will help you avoid common pitfalls and save time, money, and effort, which you can spend on refining your solution. Our AI engineers have shared some tips on how to build an AI agent they’ve learned from years of AI app development. 

Start small and scale gradually

The temptation to go big right from the start is real, but it’s also a recipe for frustration. Instead, we recommend you create a Minimum Viable Product (MVP) that will incorporate only the core functionality. For example, if it’s a chatbot, it should answer FAQs without complicating it with additional features, let’s say, sentiment analysis or multi-language support. And only after you make sure this part works smoothly and as intended you can add new features one by one. By doing so, you’ll be able to iterate quickly, gather user feedback, and make necessary adjustments without investing too much time and resources upfront.

Focus on explainability and user experience

AI can feel like a “black box” to users who don’t understand its inner workings. That’s why explainability behind AI agents’ actions is important. If your AI agent makes decisions—like recommending a product or rejecting a loan—make sure users can see the reasoning behind it and how the algorithms reach their decisions. In some industries, like finance or healthcare, decisions have significant impacts, so explainability will be indeed a crucial factor.

Equally important is the design of an intuitive user experience. An AI agent that’s difficult to interact with, slow to respond, or prone to errors will frustrate users no matter how advanced it is under the hood. Make the interface clear, responsive, and aligned with user expectations to ensure users can easily engage with it.

Regular updates and maintenance

AI agents aren’t that kind of “set it and forget it” solution. Since they don’t stop to learn and new information appears all the time, they require continuous updates to remain relevant and effective. You should train it with fresh data so it’s capable of adapting to changing user needs, market conditions, or industry regulations. Performance tuning should also be a regular task—monitor how your agent is performing and adjust algorithms or parameters as needed.

How to build an AI agent: Bottomline

The development of AI agents gradually becomes something within reach every business can afford. And conversely, what modern companies can’t afford today is to ignore the benefits and gains AI adoption promises. Tasks completed automatically that currently take several human hours to complete, as well as customer experiences that make your clients feel valued and understood, are achievable goals thanks to AI agents.  

Of course, having the right partner can make all the difference. As a trusted IT software development company with AI expertise, Relevant specializes in crafting AI agents and machine learning-driven projects that perfectly meet your business needs. 

Named a top company across several AI categories (Generative AI, Computer Vision, AI Deployment, and AI Consulting), we know how to use this technology to build practical solutions that drive real results. Whether you seek a full-cycle development team to bring your AI vision to life or a couple of extra hands for an existing project, hire AI engineers or contact us to share the details.