How to Build AI Agents: A Beginner’s Guide

Key takeaways

• When building artificial intelligence (AI) agents, you can choose to use no-code tools, utilize an SDK, or code your own agent from scratch.

• Types of AI agents include simple reflex, model-based reflex, goal-based, utility-based, and learning agents.

• Coding your own AI agent from scratch in programming languages such as Python and JavaScript gives you the ability to implement customized features into your model.

For simple tasks, you can use “agent mode” in ChatGPT to automate your workflow rather than going through the process of building an agent. Explore how to build AI agents and the core components that make them effective. If you’re ready to get your start in artificial intelligence, the Generative AI Fundamentals Specialization from IBM will give you the opportunity to learn prompt engineering techniques and gain skills in deep learning, large language modeling, and more.

What is an AI agent?

An AI agent, or agentic AI, is an autonomous AI system that can reason and perform tasks on its own. Although this may sound similar to generative AI models, the difference is that an AI agent can perform tasks on the user's behalf by leveraging tools like natural language processing, machine learning, and integrated software systems. This lets an AI agent observe, reason, decide, and take action, rather than generating content in response to a prompt.

AI agents fall into several categories, and their complexity and capabilities determine their classification. Each type of agentic AI has unique strengths, making it an ideal fit for certain use cases, or you can use them together in a multi-agent system. Common use cases include automating tasks in software development, customer service, supply chain management, and even health care, where doctors can use AI agents to help diagnose patients and establish treatment plans [1].

The five types of AI agents available today are simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents [2]:

What are the 5 types of AI agents?

The five types of AI agents available today are simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents [2]:

• Simple reflex agents: Useful for basic and repetitive tasks like temperature control, simple reflex agents observe conditions in their environment and respond accordingly using predefined conditions set by the “if-then” computing rule. However, these agents lack memory.

• Model-based reflex agents: Building upon simple reflex agents by tracking environmental changes and retaining these insights, model-based reflex agents can utilize additional context for decision-making.

• Goal-based agents: Capable of working toward specific objectives, goal-based agents use their reasoning capabilities to choose actions that get them closer to an established goal. 

• Utility-based agents: To maximize results, utility-based agents evaluate additional functions beyond goals, such as costs and risk factors, to determine the optimal course of action.

• Learning agents: By learning from data interactions over time, learning agents respond to new feedback and make adjustments to continue improving their decision-making capabilities.

What are 3 ways to build an AI agents?

Building an AI agent doesn’t require advanced programming skills, as you can take a no-code approach to agentic AI, or you can utilize tools like software development kits (SDKs) to help you build your agent. Alternatively, you can also code AI agents from scratch. Discover the key advantages of each [3].

No-code

Thanks to no-code tools, you can build AI agents without advanced technical skills or formal training in software development. Through plain language instructions, automation, and visual aids like drag-and-drop features, you can build your own applications and automation systems without using programming languages. Examples of no-code platforms you can use for building AI agents include LangChain, Zapier, and Clay.

SDK/Framework

SDKs and frameworks are platforms containing the resources you need to build and deploy different types of tools, including AI agents. They simplify and speed up the process by providing the code, libraries, compilers, and guidance you need. For example, OpenAI's Agents SDK gives you the infrastructure you need to build AI agents for use cases such as code editing and file inspection.

Scratch coding

Using programming languages like Python and JavaScript, you can code your own AI agents from scratch. Even with the availability of no-code platforms and SDKs, scratch coding still has its place in building AI agents since it gives you more development options in terms of customizability, as well as greater awareness of the inner workings of your agent.

What are the core components of an AI agent?

AI agents feature several core components, including memory and reasoning, that give them the ability to perceive, recall information, reason, plan, and take action independently [4]:

• Perception: The first component of an AI agent is the input processing layer, where the agent gathers inputs from a variety of sources and analyzes the information.

• Memory: By retaining what it learns, agentic AI can understand context in longer interactions and keep learning through continued use. Short-term memory helps recall information in an ongoing conversation, while long-term memory supports personalization and decision-making.

• Reasoning and planning: Agentic AI can analyze information using a large language model (LLM) and consider multiple solutions, whether through rule-based systems or more complex deep learning algorithms, and establish the best course of action to reach the desired outcome.

• Action: After establishing a plan, the agent can now perform tasks and interact by utilizing specific workflows. It ultimately evaluates the success of the results to identify opportunities for improvement.

How does the build process work?

1. Define the agent's purpose: Establish what you want the agent to do and identify the capabilities it needs. This will help you understand the key capabilities and features you want to include when building your agent.

2. Choose your tools and approach: Select frameworks and determine the machine learning approach you'll use, including details such as the type of machine learning approach you will implement for training your model, whether that's through reinforcement learning or neural networks.

3. Prepare high-quality data: Acquire accurate, clean data before building.

4. Build and train the agent: Develop the agent and adjust its parameters as you train it.

5. Test the agent: Evaluate it across different scenarios and reduce errors before deployment. As you work through the training process, you will want to continually adjust the parameters to reduce errors and regularly test the agent in various scenarios to ensure it's ready for deployment.

6. Monitor performance: Continue tracking how the agent performs over time.

What is an example of a simple AI agent in action?

An example of an AI agent in action that you can see today is autonomous vehicles (AVs). These self-driving cars can make decisions on the road using multimodal processing systems. Agentic AI can identify the optimal route, monitor traffic to make safe driving decisions using visual and audio inputs, and even predict traffic patterns. This is possible thanks to several systems working together, including a decision-making framework that can assess risk and predict other drivers' behavior, sensors around the vehicle that precisely measure distances, and a visual processing layer that tracks objects in real time and recognizes weather conditions.

Read more: 53 Artificial Intelligence Use Cases + How to Get Started

Can ChatGPT build an AI agent?

Yes, OpenAI’s Agents SDK lets you build an AI agent. The platform also allows you to add explicit guidelines and controls to monitor the agent’s performance. If your tasks are relatively simple, rather than building an AI agent, you can use the “agent mode” in ChatGPT to automate tasks. All you have to do is specify the task, such as creating spreadsheets, analyzing data, performing research, or scheduling meetings [5].

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