How to Build an AI Agent

All things AI have become a major part of various industries, especially in business, since the popular release of OpenAI’s ChatGPT. From simple question-answer formats, AI is now gearing toward AI agents that can handle tasks as efficiently as humans do.

About 64% of businesses expect AI to boost productivity, while 25% of companies turn to AI due to a lack of human resources. On top of that, the global AI market is expected to reach $2,575 billion by 2032.

If you want to use an AI agent system in your business, you have to build one first. The question is: how do you create an AI agent?

In this post, we’ll show:

• What an AI agent is and how it works
• The existing types of AI agents
• The step-by-step process to build an AI agent system
• How AI agents differ from AI chatbots
• Two different AI agent development frameworks you can use

Let’s begin.

What Is an AI agent?

An AI agent is essentially a computer program that can act on its own to achieve specific goals that you set. Here is a basic breakdown of what an AI agent is:

• Acts autonomously. While human input is still needed, AI agents can mostly operate without intervention. They can perceive their environment, collect relevant information, and make decisions based on their programmed goals.
• Consistently improving. AI agents are designed to learn from previous interactions with the data you provide them and from their environment. Human testers can also provide feedback to the agent, which it can use for improvement.

You can develop AI agents and use them in various applications, such as customer service, fraud detection, and healthcare. One of the cost- and time-effective ways to develop an AI agent is to partner with an outsourcing company that provides generative AI services.??

Types of AI agents

There are many kinds of AI agents that you can choose from, depending on your industry and application. Here are the main types:

• Simple reflex agents. These are the most basic types. They react to their environment based on pre-programmed rules.
• Model-based reflex agents. A step up from simple reflex agents, these build an internal model of their environment. Instead of simply reacting to triggers, they refer to their model before acting.
• Goal-based agents. These agents have specific goals in mind and actively work towards them. They can plan their actions and consider different options to achieve their objectives.
• Utility-based agents. These agents evaluate different options based on a predefined measure of “goodness” or utility. For instance, a recommendation system might consider various factors like user preferences and product popularity to suggest items you’d find most useful.
• Learning agents. As the name suggests, these agents can improve their performance by learning from experience. An AI spam filter that gets better at identifying spam emails as it sees more examples is a learning agent.

How Do AI Agents Work?

AI agents can perform their tasks autonomously for several reasons. Here’s a basic breakdown of how they work:

1. Training on data. AI agents train on massive amounts of data. This data could be anything relevant to your product or service, such as customer purchases, website traffic, user preferences, or healthcare records.
2. Finding data patterns. Over time, the agent finds patterns in data that will give it insights into how things work. For example, an AI agent in an e-commerce store might discover that people who buy running shoes often buy them with sports socks, which it can use for product recommendations.
3. Performing actions and goals. Based on what they learn and what you instruct them to do, AI agents take actions that meet your expected outcomes. For instance, if you want to build your own custom AI agent for customer service, it can automate ticket handling and handle first-level complaints for your human staff.

How to Build an AI Agent: Step-by-Step Guide

If you want to bring the benefits of AI into your business, you need to create your own AI agent first.

Step 1: Define the task and environment

Before anything else, identify what kind of environment you’ll put your agent in. Decide whether you’ll integrate it into an app, a website, or any other system. This way, you ensure the AI agent will be compatible with its surroundings once implemented.

After that, determine what tasks you want the agent to handle. These will vary depending on the industry.?

Step 2: Gather data

As mentioned above, AI agents rely heavily on data to train and improve. Ensure that the data is as relevant to your specific goals as possible. For example, if you want the agent to manage patient health records, prepare that data. Additionally, ensure the data is clean, well-structured, and easy for the agent to process.

Step 3: Select your tech stack

There’s no one-size-fits-all tech stack, and yours will depend on your specific goals and the environment where your agent will be deployed.

Programming language

The programming language is the foundation of your AI agent’s code. In general, you want to choose your programming languages, such as Python and Java, based on the technology that you will use. To be more specific, you can employ the following technologies in your AI agent:

• Machine Learning. Learns from data to predict and uncover patterns.
• Natural Language Processing. Enables machines to understand and respond to human language.
• Computer Vision. Computer vision gives machines the power to see and understand the visual world.
• Robotic Process Automation. Automates repetitive tasks in digital systems.

Depending on the application, you might need to use more than one technology to build your agent.

Scalability and maintenance

As your agent interacts with more people and collects more information, it will need to process and store this data efficiently. You can opt for cloud-based platforms to store your data for easier scaling in the future.

Additionally, as your agent evolves and new requirements emerge, you’ll need to be able to modify and improve its code. Consider the technical skills of your team before choosing the platform.

Step 4: Assemble your developer team

Once you have gathered your data and decided on your technology, you need to build your team to develop the AI agent. Here’s who you’ll likely need on your team:

• Machine Learning Engineer
• Data Scientist
• Software Engineer
• UI/UX Designer
• DevOps Engineer

You can either hire these roles in-house or outsource the development work. Outsourcing can be a good option, particularly when your budget is tight, your internal team is small, or the skill sets needed don’t perfectly match your existing resources.

Step 5: Design the AI agent

Work with your team to design the agent. In general, you need to decide about the agent’s build, how you will handle and process data, and consider user experience.

Agent architecture

Choosing the right architecture for your AI agent will define how easy it will be to maintain it in the future and how efficiently it can run. There are two general options that you can consider:

• Modular design. You’ll create multiple parts of your AI agent separately before assembling it into one working piece. This makes maintenance easier.
• Concurrent architecture. If you need the agent to run several tasks simultaneously, use a concurrent design.

Data handling

Define how your agent will get data. For example, you can design a chat interface where a user can enter information for your agent to process. Similarly, determine how the agent will respond. For instance, you can set it to reply to a user or update a spreadsheet based on the processed data.

User experience

If you’re having your AI agent interact directly with users, design its appearance. Use buttons, colors, and text that reflect your brand. Don’t forget to add accessibility features, like text-to-speech and more.

We also recommend including a feedback mechanism in the AI agent. This way, users can freely provide feedback, which you can use to improve the system.

Step 6: Test the AI agent

Like any complex system, thorough testing is crucial for your AI agent’s success. Testing helps identify glitches, biases, or unexpected behavior in your agent. It also highlights areas where the agent’s interaction with users can be improved.

You can perform the following tests on your AI agent:

• Unit testing, which involves testing individual modules of the agent’s code to ensure they function correctly in isolation.
• Integration testing to verify how different parts of the agent work together seamlessly.
• Functional testing to check the overall functionality of the agent against its intended use cases.
• Usability testing, which involves observing real users interacting with the agent and identifying any usability issues.

Optionally, perform edge case testing to see the boundaries of your AI agent by feeding it unexpected or extreme inputs.

Step 7: Deploy and monitor your AI agent

The last step is integrating the AI agent with your existing systems and workflows. If it will handle sensitive data, make sure that you implement proper security measures to protect it and prevent unauthorized access.

To ensure your AI agent performs at its peak, monitor it regularly. Track key metrics like accuracy, response times, and resource usage to identify performance issues. Alternatively, actively gather user feedback to understand how people interact with the agent and identify areas for improvement.

What’s the Difference Between an AI Agent and an AI Chatbot?

AI chatbots and agents might have overlapping characteristics in some aspects, but they are different. Both can use Natural Language Processing to understand text and may rely on similar Large Language Models (LLMs) for their responses.

The key difference lies in their actions.?

AI chatbots directly interact with the user either through text or voice. They can help you retrieve answers to questions and assist with some low-level tasks, but they cannot take independent actions.

On the other hand, AI agents can act autonomously. You can set them up with or without a user interface, since their goal is to complete the tasks you want them to perform.

2 AI Agent Development Approaches

You can develop your AI agents in two different ways, depending on your budget, time, and resources.

1. Build an AI agent from scratch?

Building your AI agent from scratch allows you maximum control and flexibility over its functionality and design. This approach is ideal if you need to customize the agent for specialized tasks in your business.

However, it requires significant expertise in Machine Learning and Software Engineering. In addition to skill requirements, note that building from scratch consumes more time and makes the development process complex.

2. Utilize existing orchestration frameworks?

These frameworks provide pre-built components for common AI agent functionalities. They often leverage Large Language Models for core capabilities. Here are a few popular options:

• Microsoft Autogen: Known for its easy collaboration features and simplified agent building.
• LangChain: An open-source framework offering a modular architecture for your agent.
• LlamaIndex: Suitable for tasks related to information retrieval.
• crewAI: A paid builder platform with pre-built components and tools for building AI assistants.

Final Words

If you want to enhance the productivity and efficiency of your business, building AI agents is definitely an option to consider. Now that you know the different types of AI agents, their working mechanisms, and the step-by-step process to build them, you can tailor an AI solution that fits your specific needs. Whether you choose to build from scratch or use existing frameworks, the potential benefits are immense.

Ready to elevate your business with AI? Contact us for a free consultation on AI agent development!

FAQs

What is the cost of creating a complex AI agent?

The cost of building a complex AI agent depends on its framework and complexity. In general, custom AI solutions may cost between $6,000 to over $300,000, inclusive of development and implementation. If you are outsourcing, the actual AI cost of your project can be significantly higher or lower, depending on your chosen company.

How to train a GPT agent?

Training a GPT agent involves collecting a large, diverse text corpus. Next, preprocess the data by tokenizing the text using suitable methods such as Byte-Pair Encoding, cleaning and normalizing it, and splitting it into training, validation, and test sets. Then, choose an appropriate GPT model size (e.g., GPT-2, GPT-3) and configure hyperparameters like learning rate and batch size. Train the model on GPUs/TPUs using the preprocessed data and monitor its performance with the validation set. Finally, fine-tune the model on specific tasks or domains as needed.

What kind of data does an AI agent need to function?

An AI agent needs training data, including texts, images, videos, sensor data, and other structured data, during development. This training data should be directly relevant to your industry and application. Once deployed, the agent can also collect operational data, such as user interactions and environmental data, to improve itself.