How to build an AI agent from scratch: A step-by-step guide and helpful tips

What is an AI agent?

An AI agent is a software system that leverages artificial intelligence to pursue specific goals and complete tasks on behalf of users. At its core, an AI agent (including LLM agents) is designed to help people by answering questions, automating routine work, or making complex decisions. Contrary to traditional software, they can reason, plan, memorize, and even have a level of autonomy that enables them to make decisions with minimal human oversight.

Modern agentic AI uses advanced models to process different types of data–including text, voice, and images–allowing it to converse naturally, learn over time, and coordinate for complex workflows.

They are found everywhere, from chatbots in customer service and automated schedulers in healthcare, to financial assistants monitoring market trends. The effectiveness of an AI agent depends on its design, the quality of data, and how well its algorithms are tuned. Training an agent involves collecting data, model training, and ongoing updates to ensure performance. Explore how AI agents work in more detail.

How are AI agents made?

When building agents, there are generally two primary paths: creating them from scratch or using pre-built frameworks.

The best approach for you will depend on several factors, such as your available budget, project timeline, and the extent to which your use case requires customization.

Let’s compare these two options by looking at their pros and cons.

To sum up, building agents from scratch is the go-to option if you have ample resources and know-how. Meanwhile, a pre-built framework is the best choice if you have a limited budget and experience developing AI tools. For example, nexos.ai Agents are completely no-code, so that non-technical teams like Marketing, Sales, Operations, and more can get started with task automation in minutes.The platform also offers an extensive library of Agent templates, so that you can reuse proven Agents built by experts. 

Building an AI agent from scratch 

Building an AI agent from scratch begins with defining the goals and gathering relevant data for training. Then comes the part of designing and implementing custom algorithms, machine learning models, and decision-making processes. Each part, from how the agent sees inputs to how it interacts with users, is built for a particular purpose, offering flexibility over every feature.

It usually involves a team of artificial intelligence, software engineering, and data science experts. The process may include collecting and cleaning data, building or selecting appropriate machine learning models, and continuously training, testing, and refining the agent’s performance. Developers must also create the infrastructure for deploying, monitoring, and updating the AI agent over time.

While building from scratch can be complex, it allows organizations to develop highly specialized agents that can adapt to the ever-changing business needs. By handling every aspect of development internally, teams can experiment with novel ideas and push the boundaries of what their AI agent can achieve.

Building an AI agent with pre-built frameworks

Building agents with pre-built frameworks provides developers with templates, libraries, and pre-trained models that simplify the whole process. Rather than starting from scratch, they can customize components according to their needs, often by following step-by-step guides or using drag-and-drop interfaces.

Many popular AI frameworks, such as TensorFlow, PyTorch, or Google Dialogflow, come with documentation, community support, and integration options. This speeds up the process of building AI agents, often without needing AI or data science expertise. Frameworks allow developers to focus more on user experience, integration, and testing.

Even though the framework handles the main infrastructure, developers can still customize certain features and behaviors, ensuring the AI agent meets specific business goals.

Can you build an AI agent on your own?

Yes, you can build your own AI agent without hiring anyone. Here are the key steps that you should follow:

1. 1.
   Define the problem you want the AI agent to solve.
2. 2.
   Gather and preprocess relevant data for AI agent training.
3. 3.
   Select the programming language that you’ll use, such as Python.
4. 4.
   Choose a framework, such as PyTorch or TensorFlow.

Alternatively, you can learn how to build an AI agent without coding with the help of Gumloop, n8n, or Flowise AI. However, you still need software development skills to use them effectively. These skills are vital for configuring the n8n integrations list that allows your agent to plug into the right apps.

That being said, there are many online tutorials, open-source libraries (Composio, Unsloth), and communities (community.openai.com) explaining how to build an AI agent for beginners.

Whether you’re interested in creating a simple chatbot or a more advanced autonomous agent, a step-by-step approach, paired with curiosity and persistence, can help you bring your ideas to life.

What are the basics of building an AI agent?

Building and training an AI agent means teaching it to understand human language and interact with it in a helpful way. This relies heavily on your data, which serves as the foundation for the AI agent’s learning and performance.

Key concepts from artificial intelligence, especially machine learning (ML) and natural language processing (NLP), play a central role in this development. Thanks to machine learning, the agent can learn from patterns in data, while NLP equips it to interpret, process, and respond to language as humans do.

Another essential element is data labeling, where examples in your training data are tagged with correct outputs to guide the agent’s learning.

Together, ML, NLP, and well-labeled data allow your AI agent to understand user inputs, generate responses, and constantly improve its capabilities.

Machine learning (ML)

Machine learning (ML) is a key component of artificial intelligence that enables automated learning and improvement based on experience instead of programming.

When building an AI agent, ML algorithms analyze historical data, such as examples of real human interactions, to detect patterns and make informed decisions.

As the agent processes more data over time, it becomes increasingly effective at predicting and responding to user requests, continually refining its performance and accuracy through ongoing exposure to new information.

Natural language processing (NLP)

Natural language processing (NLP) enables computers to understand, interpret, and generate human language. When talking about AI agents, NLP is what allows them to process large volumes of text or speech and respond in a way that feels natural and meaningful to users.

NLP makes sure that an AI agent can interact smoothly by delivering accurate and relevant responses.

Data labeling

Data labeling is crucial in training AI agents, where humans add meaningful tags or labels to raw data to help the system learn and understand context. This process can include marking word classes in text, identifying its sentiment, or categorizing customer inquiries.

Thanks to data labeling, the AI agent can recognize patterns, understand user intent, and generate accurate responses. Ultimately, high-quality labeled data forms the foundation for an effective AI agent that can understand and interact with users in a meaningful way.

How long does it take to build an AI agent?

Building an AI agent can take anywhere from a few hours to several months. The length of the development process depends on the complexity of the project, available resources, and the approach you choose.

Simple AI agents, such as basic chatbots using pre-built frameworks, can be developed and deployed within hours or weeks. More advanced, custom agents, especially those requiring large datasets, specialized algorithms, or integration with complex systems, may take months or even longer from initial planning to full deployment.

You also have to take into account such things as data collection and labeling, model training, and testing. Additionally, building a robust, reliable AI agent requires continuous improvement based on user feedback and real-world cases.

How much does it cost to build an AI agent?

Building an AI agent can cost anything from $50 to $500,000 or even more. Therefore, those looking to develop an AI agent for free should be ready to spend at least some money.

A lot depends on the development approach, complexity, and customization needs. Let’s break down the four common scenarios and their price ranges.

• Building it yourself. According to Addlly, the expenses can be minimal (about $50/month) when using open-source frameworks, such as Haystack or LangChain, and free or low-cost APIs. However, this doesn’t include hosting prices, which can quickly go up due to increased traffic and storage needs.
• Hiring professionals. Fully outsourcing AI agent development involves planning, design, programming, model training, testing, and deployment. As per Phyniks, the prices range from $50,000 for small to $150,000+ for large AI agent projects that deal with complex tasks.
• Building from scratch. This approach gives maximum flexibility but entails higher costs. According to Creole Studios, a basic chatbot will require at least $10,000, while an enterprise-level agent's prices start from $120,000. You should also allocate at least 25% of the annual development budget for maintenance.
• Using existing frameworks. As per Rapid Innovations, pre-built AI chatbots and virtual assistants cost $20-500/month. Hosting prices are also in the same range. Finally, updates and maintenance, which are inevitable, will add 15-20% of the initial development cost annually.

8 steps to build an AI agent: a tutorial 

Building an AI agent from scratch might initially feel overwhelming, but dividing the process into clear, manageable steps makes it much more approachable.

Below is a step-by-step guide that covers everything from clarifying your AI agent’s purpose to deployment and ongoing maintenance.

Step 1: Define goals and scope

The first and arguably most important step in agent development is to define its goals and scope. Begin by deciding exactly what you want your AI agent to accomplish. Are you looking for an agent that can analyze customer interactions, automate repetitive business processes, sort documents, or generate actionable insights from large datasets? Being specific about your expectations will help shape the entire development process.

Next, what core capabilities does your agent need to have? Should it act as an online shop assistant or maybe provide business insights?

Also, what is your main goal? Is it improved efficiency, customer service, or decision-making? Identifying the desired results will help you focus on the features that are needed to achieve them.

Then, consider what kind of data your agent will use. This could range from structured databases to unstructured information in emails or chat logs. Clearly defining the data sources early on ensures your agent is set up to learn and function effectively.

Think about the level of autonomy your agent should have. Should it make decisions independently, or operate under human supervision? Defining this will influence how much control you retain and how much responsibility the agent can handle. Don’t forget to consider any ethical or compliance requirements, especially if your agent will handle sensitive data or operate in a regulated industry, such as healthcare.

Finally, keep your target audience and main use cases in mind. Remember that different users and industries have their own needs. 

Step 2: Build the development team

Once your goals and scope are defined, the next step is assembling the right development team.

Creating AI agents is a challenge that usually requires at least some of the following specialists:

• Machine Learning Engineer. Develops the algorithms and models that act as the foundation of your AI agent.
• Data Scientist. Responsible for preparing, labeling, and analyzing data to ensure the agent’s smooth learning process.
• Software Engineer. Integrates the AI agent with other systems, develops backend infrastructure, and ensures smooth operation.
• UI/UX Designer. Creates intuitive user interfaces and seamless user experiences, making the AI agent accessible and user-friendly.
• DevOps Engineer. Manages deployment, scaling, and ongoing maintenance to ensure your agent runs reliably and efficiently.

Filling these roles in-house gives you more control over the project. However, outsourcing can be a good choice if you have a limited budget, a small internal team, or lack certain skills. Meanwhile, opting for a hybrid approach can get the best of both worlds.

Step 3: Collect, clean, and prepare training data

AI agents depend on accurate, unbiased, and relevant data to learn and perform well. You can gather it from:

• Internal sources. Sales records or customer profiles.
• External sources. Purchased datasets or publicly available information.
• User-generated content. Social media posts or product reviews.

Also, consider collecting text transcripts from chat logs, emails, support tickets, and voice recordings if your agent must understand spoken language. Moreover, interaction logs from similar systems can provide valuable insights about user behavior.

The next step is to clean and preprocess your data. It includes removing irrelevant or incorrect information, fixing errors, adding missing values, or filtering out noise from audio files.

Finally, don’t forget to label your data. Identifying user intent and categorizing requests helps your AI agent understand the context behind the user queries. It’s a crucial part of its training, allowing the agent to learn from real-world interactions.

Step 4: Choose AI tools and models

Your choice of AI tools and models depends on what your agent needs to do, the type and amount of data, and your deployment requirements.

Start by choosing the machine learning model. If your AI needs to understand and generate human-like language, neural networks, especially large language models, are often the best fit. Neural networks are great at processing massive amounts of data, recognizing complex patterns, and generating quality outputs.

Reinforcement learning can be a good choice for agents that make sequential decisions or adapt based on ongoing user interactions. It enables learning by trial and error while using feedback.

When choosing the right AI technologies, consider the frameworks and libraries that match your needs and expertise. For machine learning, popular frameworks like PyTorch or TensorFlow offer flexibility and robust community support.

If natural language processing (NLP) is central to your agent, libraries, such as NLTK or spaCy, provide powerful tools for text analysis and language understanding. For agents dealing with visual data, frameworks like Keras or OpenCV, especially with pre-trained models, are well-suited for image recognition and processing.

Pre-trained models can serve as a strong foundation, especially for language-based agents. Options such as GPT (Generative Pre-trained Transformer) are great at generating text and can be customized for answering questions, creating content, and similar tasks.

Meanwhile, BERT (Bidirectional Encoder Representations from Transformers) is effective for understanding context within language. This makes it valuable for tasks such as sentiment analysis or translation. However, to maximize performance for your specific application, you’ll likely need to fine-tune these models with your own labeled data so the AI adapts to your domain’s nuances.

Deployment options are also important to consider. You can host your AI agent on-premise for greater control and data security, or use cloud platforms like Google Cloud or AWS for scalability and easier management. Edge computing is another option if your application demands real-time responses close to the data source.

Finally, think about your development environment and data management. Integrated development environments (IDEs) like VS Code or PyCharm can streamline your workflow, while data management tools such as MongoDB or Apache Kafka help with storage and data pipelines.

Step 5: Design your AI agent

Start the design part by determining your agent’s architecture. It’s the backbone of your AI agent, dictating how different components work together.

If you choose a modular design, you can develop and test individual components, such as input processing, data analysis, and output generation, separately. This makes it easier to update, debug, and scale your agent.

Alternatively, a concurrent design lets your agent perform multiple tasks at the same time. This is crucial for applications like real-time customer support.

Next, define the agent’s core functionalities. Clearly outline your agent's primary tasks, from receiving and preprocessing data to analyzing inputs, making decisions, and generating outputs such as recommendations, classifications, or direct actions.

Think carefully about how users will interact with your AI agent. Will it be via a web interface, a chatbot, or maybe an API? Incorporating feedback mechanisms, like user ratings or automated performance monitoring, will help your agent learn and improve over time.

Plan the flow of data through your system. Decide how your agent will handle incoming information, including the formats it needs and any preprocessing required for accurate results. Map out the internal logic for processing and transforming data, then specify the types of outputs your agent will deliver and how these will be communicated back to users or integrated systems.

Finally, determine your agent’s decision-making processes. Choose algorithms that best suit your goals, whether it’s neural networks for complex pattern recognition or decision trees for simpler, rule-based tasks. If your agent uses reinforcement learning, clearly define the policies or rules that will guide its behavior as it interacts with users and adapts to new situations.

Step 6: Train the AI agent

Follow these steps to train your AI agent effectively:

1. 1.
   Set up your environment. Ensure you have all the necessary tools and frameworks installed, such as TensorFlow or scikit-learn, depending on the requirements of your project.
2. 2.
   Load your data. Bring your cleaned and labeled dataset into your workspace so it’s ready for use in the training process.
3. 3.
   Split the data. Separate your data into two subsets — a training set and a test set. The former is used to teach the model, while the latter will later help evaluate its performance on unseen data.
4. 4.
   Select and initialize a model. Choose the appropriate machine learning model architecture for your problem and initialize it in your environment.
5. 5.
   Configure training parameters. Set key parameters such as learning rate, batch size, and the number of epochs. The learning rate dictates how quickly the model updates. The batch size specifies how many samples the model processes before updating. The number of epochs is how many times the model passes through the entire training dataset.
6. 6.
   Start training. The model will process the training data, update its weights, and attempt to minimize errors.
7. 7.
   Monitor progress. Keep an eye on important metrics like accuracy and loss. This will help you spot issues early on. If you notice problems (like the loss not improving), consider tweaking parameters such as lowering the learning rate or increasing the number of epochs.

Step 7: Test and improve

Here’s how you should test and improve your AI agent:

1. 1.
   Choose a testing method:

   • Unit testing. Isolate and check each component (like intent recognition or response generation) to make sure that every one of them works correctly on its own.
   • User testing. Involve real users and allow them to interact with your AI in a controlled setting to uncover usability issues and real-world challenges.
   • A/B testing. Run experiments comparing two versions of your agent to see which version yields better results.
2. 2.
   Evaluate performance. Measure key metrics, such as task accuracy, response time, and user satisfaction. Check whether the agent consistently gives correct answers and operates efficiently.
3. 3.
   Watch for overfitting. Be alert to cases where your AI agent excels with training data but struggles with unseen examples. To avoid this, use strategies like cross-validation, which involves rotating your training and testing sets to check that your model is good at generalizing.
4. 4.
   Iterate and improve. If your AI isn’t working as expected, return to the training phase (Step 6). Adjust the parameters, increase the amount of training data, or retrain from scratch.
5. 5.
   Gather user feedback. User input helps improve AI’s responses and interface. It’s also valuable if you need to retrain the model with richer or more diverse data.

Be aware that testing and improving are ongoing processes. By evaluating performance and listening to user input, you’ll make certain that your AI agent delivers the best results in real-world scenarios.

Step 8: Launch and monitor

Before going live, test the agent in an environment that resembles the real world. This way, you’ll be able to catch last-minute issues and make sure that your agent performs reliably when released.

Also, consider step-by-step deployment: Start by launching your agent to a small group of users, collect their feedback, and use it to improve the final version.

After going live, don’t forget to set up extensive monitoring. Track metrics like response times, accuracy, success rates, and user satisfaction. Use real-time analytics and error logging to troubleshoot issues. Also, enable alerts for spikes in errors or dips in performance.

Collect feedback with the help of ratings, comments, or surveys. Pay attention to direct feedback and behavioral data to identify areas for improvement.

Update your agent regularly to enhance its capabilities, fix bugs, adapt to user needs, and address vulnerabilities.

Most importantly, remember that a successful launch isn’t the end. Ongoing monitoring and iteration will keep your AI agent effective, ensuring it delivers value and a great user experience over time.

8 tips to build an AI agent seamlessly

Finally, we want to give some tips on building agents to help avoid costly mistakes, especially if it’s your first time developing such tools.

1. 1.
   Start small and simple. Avoid feature overload and focus on proving your concept.
2. 2.
   Understand the problem. Clear objectives help save time and money.
3. 3.
   Don’t build from scratch (unless you need to). Use existing models, datasets, and frameworks when possible to make things easier.
4. 4.
   Use quality data. Clean and relevant data leads to better model performance.
5. 5.
   Improve incrementally. Analyze mistakes and iterate – no AI agent is born perfect.
6. 6.
   Document your work. Documentation of your code and findings is key to tracking progress and debugging issues in later stages.
7. 7.
   Respect privacy and ethics. Ignoring these can cost you money, and, most importantly, your reputation.
8. 8.
   Consider an AI platform. If you’re building or using multiple agents with different agentic AI use cases, a platform like nexos.ai can help scale and organize your infrastructure. It also integrates well with LangChain and other agentic frameworks.