Right now, AI can be likened to a vital tool in a gold rush, offering significant potential for those who adopt it. Therefore, in recent months, numerous businesses have reached out to us, eager to integrate artificial intelligence into their custom software solutions. We often suggest that these entrepreneurs develop an AI agent — an AI system that autonomously fulfills business tasks. Over this period, we've gathered extensive information about the types of AI agents and the techniques to build and train them. That's why we want to share this knowledge with you. In this article, we explain the different methods and technologies for building an AI agent and offer a detailed guide on how to build a custom AI agent from scratch.

What is an AI agent?

An AI agent is a system designed to perform tasks automatically. These agents possess the capability to perceive their environment, process information, and take actions aimed at achieving specific goals. By integrating advanced technologies such as machine learning models, large language models, and other AI tools, an AI agent can make decisions that traditionally required human supervision and manual work.

AI agents do more than just perform tasks — they can also learn from past interactions and outcomes to improve their future performance. This adaptability is achieved through machine learning techniques that allow the agents to adjust to new situations in real-time.

Here are two examples of how agentic AI can be used in various spheres. One example is an AI-powered recommendation system used by Netflix that analyses viewing habits and suggests content tailored to individual users. Another example is a smart irrigation system that uses machine learning to analyze soil moisture, weather forecasts, and crop water needs and makes decisions about irrigation. These examples underscore the diverse fields and functional applications where AI agents are employed.

Regardless of the field or functionality, AI agents offer businesses similar benefits. They automate routine and repetitive tasks, freeing up human employees to focus on more complex tasks. Moreover, agentic AI can analyze and take advantage of large data volumes, providing insightful analytics and decision-making support. AI-powered startup software solutions are 24/7 available, which enables continuous support and service delivery, while their scalability allows handling increased workloads or expanded operations seamlessly.

AI has a wide range of applications beyond its current uses. Recently, we researched AI adoption and its target audience to comprehend how people use AI and explore its potential business applications. To learn more about our findings, follow the link and read our article on the adoption of generative AI technology.

Types of AI agents

When considering how to build a custom AI agent, it's important to understand the different types of AI agents available, as each is typically suited to specific tasks. Here's the list of possible agent types and ways to apply them:

Simple reflex agents. These are basic AI systems that operate based on predefined rules to respond to environmental conditions, without memory or learning capabilities. They're ideal for straightforward tasks like customer support chatbots, which use preset answers to address user queries. Other applications include automatic doors that open with motion detection or spam filters that identify unwanted emails by recognizing specific patterns. These AI agents are effective for simple, predictable tasks but aren't suitable for complex environments.

Model-based reflex agents. These are advanced AI agents that use an internal model of the environment to predict how actions will affect future states. They are used in scenarios requiring an understanding of environmental dynamics, such as self-driving cars, smart vacuum cleaners, irrigation systems, and home automation systems. These agents analyze data and adapt their actions based on past interactions and current conditions.

Goal-based agents. These AI agents focus on achieving specific objectives. Unlike other agents that react to environmental triggers, these agents use planning capabilities to identify the best paths to their goals. They're crucial in applications like autonomous vehicles and robotics, where goal attainment is essential. Examples include project management software that optimally schedules tasks and allocates resources, as well as navigation systems that determine the most efficient routes by considering traffic and road conditions.

Utility-based agents. Unlike other AI agents that merely achieve set goals, these agents consider multiple factors to optimize the outcome of a task. They strive to maximize overall benefit by evaluating a range of criteria simultaneously. In self-driving cars, agents accomplish a complex task and balance objectives like minimizing travel time, maximizing fuel efficiency, and providing safety. In e-commerce, these agents can optimize pricing and product recommendations by analyzing sales history, customer preferences, and inventory levels.

Learning AI agents. Learning agents are advanced AI systems that autonomously complete tasks and refine their abilities by learning from past experiences. These agents update their behavior based on environmental feedback, which improves decision-making in dynamic situations. They are used in various applications, such as optimizing content recommendations on social networks, acting as virtual assistants in customer support, analyzing patient data in healthcare, making investment recommendations in fintech apps, and accomplishing many other tasks.

Building AI agents encompasses a variety of types, each suited to different applications and complexities. Besides the commonly discussed types, there are hierarchical agents, multi-agent systems, and robotic systems. These advanced AI systems are integral in industries such as manufacturing, healthcare, agriculture, and other fields requiring complex, custom AI solutions.

In summary, AI agents are categorized based on their intelligence level, decision-making processes, and their ability to perceive environmental conditions. The six main types — simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, learning agents, and multi-agent systems — each possess unique strengths. These diverse agent types are employed to tackle various business tasks and simplify everyday life.

Build a custom AI agent vs. use low-code solutions

When considering how to build a custom AI agent, there are two major approaches. One involves using low-code solutions, while the other involves developing a custom AI agent from scratch using available software development technologies.

Low-code software development helps to create applications through template builders and drag-and-drop tools, which eliminates the need for hand-coded programming. This method offers businesses the opportunity to build, test, and refine AI models rapidly, speeding up the development process and making it more accessible to a wider range of users.

Major tech giants released several low-code solutions for AI agent development. These platforms enable businesses to create AI agents using visual tools and pre-built components. Among the most popular low-code solutions are those provided by IBM, Google, Amazon, and Salesforce. Let's take a closer look at one of them.

Vertex AI agent builder by Google

When thinking through the question of how to build a custom AI agent, Google's Vertex AI Agent Builder is an option to consider. It is designed to help users develop autonomous AI agents powered by generative AI. This tool enables businesses to get systems with diverse functionalities, from chatbots to fraud detection systems. Key features of the Vertex AI Agent Builder include:

• No-code console: Users can design, deploy, and manage AI agents without hand-coding, using prompts and natural language. For those with more advanced development abilities, the AI tool also provides pre-made components that simplify the development process.
• Seamless integration: Connect your AI solution to enterprise systems and existing data sources to provide more accurate results. This capability helps custom AI agents be relevant to your business operations.
• Google Cloud infrastructure: Deploy your custom AI agent within Google's cloud infrastructure that benefits from high security standards and reliable performance.
• Memory management: Once in production, an intelligent agent is equipped with both short-term and long-term memory. This allows it to manage sessions and recall past conversations and preferences, which allows users to continue the conversation, not starting it from a blank page.
• Retrieval-augmented generation: This feature empowers agents to access and interpret organizational knowledge intelligently. Intelligent agents can connect to various data sources, including local files, Cloud Storage, Google Drive, Slack, Jira, and more.

Tools for AI agent development from other companies offer distinct advantages. IBM Watson excels in conversational AI. It uses advanced natural language processing for tasks like customer service and HR communication. Amazon SageMaker is notable for providing end-to-end AI development resources and integrating with AWS services, which is especially useful for financial services, healthcare, and retail sectors. Agentforce, an AI technology from Salesforce, stands out for its integration with CRM systems. It provides quick AI adoption and helps with lead prioritization, sales forecasting, and improved customer interaction.

However, there are cases where low-code solutions provide the most benefit for AI agent development and situations where they may not be the best fit. Here's a breakdown of these scenarios.

Cases when low-code solutions should be used:

Lack of technical expertise: If you lack partners or employees with programming or AI development skills, low-code platforms offer an accessible way to build and deploy AI agents. These platforms simplify the process, allowing non-technical users to create functional AI tools.

Quick launch required: When you need to quickly check your idea's feasibility on the market, using low-code solutions is beneficial. They allow for rapid development and iteration on AI solutions, cutting time on developing custom design, coding, and testing of the custom AI agent.

Simple product development: Low-code solutions are ideal for developing simple AI agents, such as chatbots or basic AI assistants. These applications don't require the extensive customization or complexity that more advanced solutions demand.

Cases when low-code solutions may not be suitable:

Complex and customized solutions: If you're planning to build a complicated product, such as an advanced autonomous AI agent for health data or trade market analysis. These projects often require investment in the services of third-party technical teams.

Commercial AI solutions: If your business idea involves selling AI-powered SaaS products, be aware that many low-code platforms have restrictions on commercial use. This limitation could prevent you from fully realizing your business model.

Vendor lock-in: Depending heavily on a specific low-code platform can lead to vendor lock-in, making it difficult to switch providers or adopt new technologies as they emerge. This can limit your opportunities for innovation in the future.

The Vertex AI Agent Builder from Google provides a low-code platform for developing autonomous AI agents. Featuring a user-friendly no-code interface, it facilitates easy integration with existing business systems and uses Google's secure cloud infrastructure. With this tool, users can create diverse applications such as AI chatbots and fraud detection.

Low-code solutions are beneficial for teams without deep technical expertise, those requiring quick market entry, or those creating simple products. They offer ease and speed in development but may not suit complex, highly-customized AI solutions or commercial projects. In such cases, knowing how to build custom AI agents with available technologies may be advantageous.

A brief guide on how to build a custom AI agent

Creating a custom AI agent involves several key steps to make sure the agent functions effectively and meets the desired objectives. Here's an overview of the process:

1. Define the AI agent's objectives

Just as with any custom software solution development, defining the objectives for your custom agent is a critical first step before initiating the development process. These objectives act as a strategic guide and influence technology choices, design decisions, and implementation steps. This procedure helps to make sure that each aspect aligns with the agent's purpose. Clear objectives also help to shape a clear vision for the custom AI agent. Key questions to address include:

What business problem will the AI solution solve? This could be reducing employee workload with an AI chatbot, improving conversion rates through personalized content recommendations, or managing leads more effectively with an AI-powered lead scoring system. Identifying these challenges highlights resource-intensive processes and helps find ways to cover them.

Consider what functions the custom AI agent should perform — answering text inquiries, responding to voice commands, or acting on event triggers. Investigating agentic AI use cases in various industries can clarify which functionalities might best solve your business issues.

Understanding your audience's interaction with the agent is also important when considering how to build a custom AI agent. With the help of such an analysis, you make sure that the created agent will meet the user's expectations and communicate with them in a familiar and convenient manner.

Moreover, think about which other software the AI agent should integrate with, whether it's a mobile app or a website. Research algorithms and models to determine if an AI solution is necessary or if the issue can be addressed by other means. This aligns AI development with your business objectives and technological requirements.

By the way, our team has successfully delivered AI analytics services to several business clients. We can assist you in identifying where an AI agent can be integrated within your business processes and help outline its functions by considering the capabilities of suitable AI models.

Ready to transform your business processes with a custom AI agent? Message us today to start creating an agent tailored to your unique needs!

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2. Gather and prepare data for training

In addition to developing an AI agent's interface, gathering comprehensive data to train it is crucial in the process of how to build a custom AI agent. Training refers to teaching an AI agent to perform tasks and make decisions based on data and predefined objectives. The quality and relevance of the data directly impact the model's performance and accuracy, much like how the quality of ingredients affects the taste of a dish.

Gather

Training data can be sourced from various channels depending on the specific task. Text data for natural language processing models like chatbots can be gathered from customer service logs, websites, and emails. For training speech recognition models, audio data from podcasts and call center recordings helps the AI technology learn different intonations and accents. Structured data from databases and spreadsheets can be used for prediction tasks such as stock forecasting and customer segmentation, while data from CRM can be used to prepare custom AI solutions for lead scoring.

Clean

After gathering the data, it needs to be cleaned — this procedure involves removing inaccuracies, duplicates, and irrelevant information. For text data, this often means correcting typos, standardizing formats, and filtering out irrelevant information. With images, noise reduction or resizing may be necessary. Audio data cleaning might involve removing background noise and standardizing volume levels. For structured data, this process includes checking for missing values and providing consistency across datasets.

Label

Another crucial step in training data preparation is labeling the data. This complex task involves annotating data, where annotators apply labels to the data according to specific guidelines. It can be performed manually or with the aid of automated tools. For example, text pieces like messages or reviews can be labeled with sentiments such as "positive," "negative," or "neutral." This enables the AI model to discern the sentiment of the user. Similarly, labeling helps classify intents in user messages, identifying phrases used to request information, file a complaint, or ask for support.

Labeling data of any kind is integral to the machine learning process, as it creates the relationships between inputs and outputs. This guidance allows the custom agent to learn more effectively and gain a structured understanding of data.

3. Select suitable technologies

As you move to the next step, collaborating with your tech partner to select suitable technologies is key to how to build a custom AI agent. There are different categories of technologies to consider:

AI Models:

The AI model is the central element that determines the algorithmic strategy for tackling tasks such as sentiment analysis, image recognition, or predictive modeling. This model is crucial for processing input data and generating outputs based on learned patterns from training, functioning as the AI agent's brain. Several types of AI models are available. For example, generative models are designed for generating new data samples that are similar to those in the training set, while decision trees facilitate decision-making processes. These options include random forests, support vector machines, and many other models.

Choosing the right model depends on the task your custom AI agent is intended to perform. For instance, Convolutional Neural Networks (CNNs) are optimal for image recognition, whereas Recurrent Neural Networks (RNNs) excel with sequential data, like time series or text. Each model type is suited to different purposes. Additionally, the nature and structure of your data — whether textual, numerical, image-based, or sequential — will impact model selection. By aligning your model choice with the data and goals, you set the robust foundation for AI agent development.

AI agent frameworks:

Building AI agents from scratch can be achieved using typical frontend and backend development technologies such as Python, JavaScript, Java, or C++. However, specialized AI agent frameworks offer more advantages. These frameworks are collections of tools, libraries, and interfaces designed to streamline the creation, training, and deployment of AI models. They provide an infrastructure that simplifies AI agent development, especially for tasks involving machine learning and deep learning. While an AI model performs specific tasks, an AI framework supports the development and execution of the model with a suite of tools and libraries.

AI agent frameworks optimize the development process by providing pre-built functions and reducing the need for extensive coding. This is particularly valuable for building advanced models and managing complex architectures. Furthermore, these frameworks offer capabilities for large-scale data processing, essential in scenarios that require handling vast amounts of data. Developers use frameworks to create scalable custom AI agents quickly with the help of tools and libraries provided by the framework.

Surrounding technology stack:

While the AI segment of the application is crafted using an AI agent framework, developers still need to establish the user interface with the help of tools commonly used for web or mobile app development services. This involves selecting suitable frontend, backend, or mobile technologies. Popular options include React or Angular for frontend development, Node.js or Django for backend development, and Swift or Kotlin for mobile applications.

In addition to interface development, it's vital to choose technologies for data storage. Options such as PostgreSQL, MySQL, or MongoDB are commonly used to manage and store data reliably. The selection of data processing technologies is also crucial, with frameworks like Apache Spark or Hadoop providing tools for data manipulation and transformation. For deploying the custom AI agent, cloud services like AWS, Google Cloud, or Azure can be used, they provide a scalable and stable environment. These platforms offer the infrastructure necessary to deploy a custom software solution.

4. Train the AI model

After choosing the necessary technologies, software engineers establish the functionality of the custom AI agent. They define how the agent will operate, interact with users, and connect with external systems, making sure it aligns with the project objectives using the selected AI framework. Following this, they configure databases for data storage. Developers also prepare environments for data processing and set up cloud platforms to host and deploy the AI solution.

The next key step in how to build a custom AI agent involves training it using the data you've gathered. Successfully training the model makes sure that you build an effective AI agent that is both accurate in its current task and adaptable enough to handle real-world tasks. To begin, divide your training data into separate datasets for training, validation, and testing the AI agent. This division allows you to assess the model's performance throughout training. Next, choose the machine learning model suitable for training, with options including supervised, unsupervised, and reinforcement learning.

Once the approach is selected, software engineers proceed to the actual training process. This involves feeding prepared data into the AI model to identify errors and make adjustments to increase accuracy. Upon completing this training phase, it's essential to validate the AI model with a distinct dataset. This step evaluates its performance on new, more complex data, not used during the initial training. Validation helps uncover issues like overfitting, where a model learns the training data too well, leading to poor performance on new and unseen data.

AI serves a dual purpose — not only is it valuable for fulfilling complex business tasks, but developers can also harness it to assist in creating software. If you're curious about the role of AI in software development, follow this link to read our article on the subject.

5. Test the AI agent

When testing your custom AI agent, it's vital to evaluate its accuracy. This thorough assessment helps identify any bugs before launching the agent to a broader audience. Within this step, the software engineers prepare a detailed testing suite, allowing them to test the custom AI agent in different situations.

One aspect of how to build a custom AI agent process that software engineers encounter is unit testing, where the AI agent is divided into modules to analyze and debug each component individually. Apart from that, the team should write test cases to test how the model behaves in unpredictable real-life situations. Additionally, the AI solution can be checked with simulations, where it is tested in a virtual environment close to a real one.

Once the AI agent successfully navigates scenarios and simulations, it's time for real user testing. Engaging end-users helps identify usability issues or interface anomalies. Through surveys, interviews, and direct trials with users, developers can gather valuable insights into how intuitive a custom AI solution is.

You can understand that the agentic AI meets your expectations by assessing the following diverse metrics:

• Task completion rate: Evaluate how precisely the custom AI solution performs tasks. It is one of the key metrics showing that the system functions successfully.
• Error rate: Monitors the frequency of incorrect responses or failures. This metric helps you to pinpoint areas needing improvement.
• Response time: Measures how quickly the model responds. This metric helps to find out how good the user experience will be and how smooth customer interaction will go.
• Scalability: Assesses how the custom AI agent handles varying workloads to find out how it performs under increased demand.
• Resource efficiency: Monitor the computational resources the custom agent uses. This is vital for managing operational costs.

Prepare to establish feedback loops with users to continually refine the AI agent and make sure it remains relevant to user needs and adapts to changing circumstances.

6. Integrate the AI agent and maintain it

The final step of our how to build a custom AI agent guide includes integrating it into a website or application where users can access and interact with it. This integration, achieved through comprehensive mobile or web app development services, will establish triggers to activate the autonomous agent's functionalities based on user interactions or predefined events.

Once the AI agent is live, ongoing maintenance is crucial to keep it effective and reliable. Maintenance procedures include monitoring AI technology's performance to detect any operational issues or inefficiencies. Regular updates are necessary to improve the agent's capabilities, incorporating new data to enhance its learning and adapting to changes in the environment or user needs.

Building a custom AI agent is a complex process requiring technical expertise. It begins with defining the agent's objectives and identifying the problems it will address. You'll need to gather and refine data to train an AI model, use AI frameworks to construct the agent's logic, as well as train, test, and validate it. Additionally, you'll be involved in choosing the right technology for app development, so your AI agent could be integrated into a website or application. Then the AI solution will need to be refined based on its performance to deliver better results.

Wrapping up

Clearly, building a custom AI agent presents a complex challenge. If you aim to launch a business tool of this nature, achieving success requires technical expertise from your employees, business partners, or a third-party team of software engineers. Thus, one of the most challenging aspects of the process can be finding a reliable tech contractor experienced in how to build custom AI agents. They should know how to navigate each step and procedure, guaranteeing effective development and deployment of your custom AI solution.

Looking for a tech partner to build your AI agent? Reach out to us today!

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