How to build an AI agent with ChatGPT: A step-by-step guide

An artificial intelligence (AI) agent is like a virtual assistant, except that it uses AI rather than just simple programmed responses. You can use AI agents for a host of use cases, like customer service, personal assistance, task scheduling, and so on. The best part? ChatGPT, the AI tool that most people are familiar with, can help you build your own AI agent.

Our guide walks you through the step-by-step process of how to build an AI agent with ChatGPT. You’ll learn what AI agents are and why ChatGPT is a great tool to use for this project.

What is an AI agent?

An AI agent is a software program that processes data and makes decisions autonomously. It can also learn from experience and execute actions without constant human supervision.

Some AI agents work behind the scenes, like fraud detection systems that monitor financial transactions. However, most of them engage directly with users, such as virtual assistants like Siri. These AI agents interact with users through text, voice, or both and use natural language processing (NLP) to understand user input and generate responses. They can perform several tasks, including 

• Answering customer queries 
• Scheduling appointments 
• Setting reminders
• Providing personalized recommendations 
• Managing workflows

For example, many e-commerce stores now use AI agents to support customers with their purchases. These agents can help users find products, suggest similar items, offer discounts, and process payments.

OpenAI, the company behind ChatGPT, has even developed an AI agent with e-commerce applications called Operator (more on this later). Already used by big names like Etsy, Instacart, and eBay, users can order their groceries through Operator or ask it to find a gift for their loved ones.

Why use ChatGPT to build an AI agent?

Apart from the fact that most people already use ChatGPT in their day-to-day life or work projects, plenty of other features make it a solid choice for building an AI agent. 

The first is its NLP capabilities. Since ChatGPT is trained on massive text data, it can understand and generate human-like responses to user input. So, an AI agent built with ChatGPT can also do the same.

Another significant advantage is customization. Through OpenAI’s application programming interface (API), developers can integrate ChatGPT into various platforms to fine-tune responses. They can also train the model on specific data to match their business needs. With adaptability like this, it’s possible to create AI agents for specialized tasks.

ChatGPT is also more cost-effective than building an AI agent from scratch. Since the model is already trained, developers don’t have to spend time and resources on data collection and teaching.

Such expansive data and pre-trained models also make ChatGPT more versatile. Developers can build AI agents that cater to multiple applications with varying degrees of complexity. Besides basic tasks like answering customer queries or providing recommendations, AI agents built with ChatGPT can also handle more complex functions like serving as personal assistants.

Tools and technologies needed

Here are the tools and frameworks you’ll need to build an AI agent with ChatGPT:

• OpenAI API (ChatGPT): The API will give you access to the pre-trained model and allow you to integrate it into your project. It acts as the core intelligence behind conversations, making the agent more interactive and context-aware.
• Programming languages: You’ll need a programming language like Python or JavaScript to develop and integrate your AI agent. Python is widely used for AI development due to its rich ecosystem of libraries, while JavaScript is ideal for web-based AI applications.
• AI agent frameworks: An AI agent framework provides a base structure for building and deploying AI agents. It’s sort of like a template that you can customize to suit your needs. Frameworks like LangChain help structure AI agent workflows and make managing prompts and external data sources easier. AutoGPT and AgentGPT take automation a step further by enabling self-improving AI agents that can execute complex tasks autonomously.
• Hosting platforms: You’ll need a platform to host your AI agent and make it accessible to users. Platforms like Heroku, Amazon Web Services, or Google Cloud Platform provide reliable hosting options for AI applications.
• Integration tools: Your AI agent may have to interact with other software, like a database or an email platform. Integration tools such as Zapier and Make.com facilitate the connections between your AI agent and third-party applications so that it can access and use data from various sources.

How to build an AI agent with ChatGPT in 7 steps

The section below details how to create an AI agent with ChatGPT. We’ve broken it down into just seven steps.

Step 1: Define the purpose of your AI agent

Start by deciding what your AI agent will do. What problem will it solve? 

For example, in a healthcare setting, your aim could be to create an AI agent to schedule patient appointments. If you’re building an AI agent for personal use, you may want it to help you plan your daily tasks or organize your emails.

Step 2: Set up OpenAI’s API

Next, you’ll need to sign up for OpenAI’s API and get your API key. Store your key safely, as you’ll need it to access the API to train and interact with your AI agent in the following steps.

After generating the API key, you’ll have to export it in your terminal as an environment variable. You’re now ready to make API requests in one of two ways:

1. Representational state transfer (REST) API: You can make HTTP requests to the OpenAI API using cURL, Postman, or any other tool that allows you to specify an authorization header. You can find detailed instructions on how to do this here.
2. Software development kits (SDKs): Alternatively, use one of OpenAI’s official SDKs to make API requests. These are available in popular programming languages such as Python, Java, and Node.js. You can find a complete list of official SDKs and their documentation here.

Step 3: Develop the AI agent’s core logic

The core logic of an AI agent allows it to perform the intended task. To develop this, you’ll need a dataset that contains examples of human interactions related to your task. The more diverse your dataset, the better your AI agent will perform.

You’ll need to use Python or JavaScript to develop your AI agent’s core logic. Libraries like TensorFlow can help you build powerful models for your AI agent.

Use the programming language to implement decision-making processes. Instead of providing generic responses, the agent should be able to analyze input and take appropriate actions. For example, if you’re building a customer support chatbot, it should recognize whether a user is asking about pricing or account issues and respond accordingly.

Test your agent thoroughly using different inputs and scenarios to see if it performs as intended. You may need to fine-tune your AI agent’s algorithms and parameters to achieve desired results.

Step 4: Integrate external data sources

Now, let’s say in the earlier step that one of the decision-making mechanisms was to fetch data from external sources. For example, the AI agent has to use data from your customer relationship management (CRM) system to respond to account-related queries. Integrate these external data sources into your agent’s core logic so that it can access and use them as needed.

Use APIs or SDKs provided by the external data sources to fetch and integrate the relevant data. Besides CRM systems, other examples of external data sources could include 

• Weather APIs 
• News databases
• Social media platforms 
• Product databases
• Calendar events

Step 5: Implement user interaction channels

The user interaction channel is the medium through which the AI agent communicates with users. It could be in the form of a chat interface, voice assistant, website, or email. Choose the appropriate channel based on your target audience and the platform where you plan to deploy your AI agent.

A web interface lets users interact with the AI agent through a browser. Frameworks like Flask, FastAPI, or Django help create web applications where users can type queries and receive responses in real time. For example, a customer support AI agent can be embedded into a company’s website, allowing visitors to get instant assistance.

Tools like Slack, Discord, WhatsApp, or Telegram provide seamless communication if you want to integrate your AI agent with chat platforms. Businesses use AI-powered bots in Slack to automate internal workflows, for example, while customer service teams deploy WhatsApp bots to handle inquiries. These integrations require APIs that allow your AI to send and receive messages within the platform.

For voice-based interactions, speech-to-text APIs can convert spoken language into text — just like OpenAI’s Whisper. Here, users can talk to the AI rather than type their queries. Opt for this channel if your AI agent is supposed to assist hands-free customer interactions, such as in a car or while cooking.

Step 6: Test and debug your AI agent

Before you launch the AI agent, test it thoroughly. Run simulations and look for errors and shortcomings. Note the accuracy and response times, too — users have little patience for delays and wrong answers.

If necessary, debug the code and fine-tune the algorithms to improve performance. You may also want to implement a feedback mechanism for users to report errors or provide suggestions for improvement.

Step 7: Deploy and monitor performance

Finally, it’s time to deploy your AI agent and make it live for users. You can host it on a local server if it’s for internal use or on a cloud server for wider access.

Monitor the performance of your AI agent regularly and make improvements as needed based on user feedback and data analysis. Some metrics to keep an eye on are 

• Latency 
• Accuracy 
• Engagement rate
• User satisfaction 

Prometheus is a handy tool for monitoring AI agent performance, and it also integrates with OpenAI.

Use the results of these performance metrics and user feedback to improve your AI agent over time constantly. If done right, your AI agent can become a valuable asset for you or your organization. 

Use Operator as an alternative

Building an AI agent using the OpenAI API can be a complex and time-consuming process. It requires knowledge of programming languages and NLP techniques. But there is an alternative to the API route: using OpenAI’s Operator. 

Operator is currently available to Pro users in the U.S. at the same price of $200 per month as the existing plan. It’s OpenAI’s AI agent that can perform tasks for users by going to the web itself. For example, it can 

• Search for a webpage and even interact with it by scrolling or clicking on it 
• Order things online
• Make bookings
• Run web-based tasks simultaneously
• Fill out forms

Since Operator is currently in its early stages, it has limited functionality. However, it’s constantly improving and learning new tasks — OpenAI says it can even create memes.

Operator runs on the computer-using agent model, which combines GPT-4o’s vision capabilities with reinforcement learning for advanced reasoning. It allows Operator to “see” web pages through screenshots and interact with them using the same methods a human would, such as clicking buttons.

Unlike traditional automation tools that use API integrations, Operator can navigate websites directly. If it encounters an issue, it can attempt to self-correct. However, if the task requires human input (like entering payment details), it hands control back to the user.

To use Operator for a task, simply let it know what you want it to do and it’ll get to work. You can take control of the browser anytime for tasks like entering payment details or login information. Since it lets you save prompts on the homepage for quick access, you don’t have to repeatedly type in the instructions for frequently performed tasks.

Another alternative method: Jotform AI Agents 

As helpful as OpenAI’s Operator is, it’s limited to web-based tasks. Jotform, on the other hand, offers AI agents that can automate many different functions — including web-based and document-based ones — in a wide range of industries.

Jotform AI Agents can train on any data, such as your organization’s knowledge base or existing documents. They then transform your forms into smart forms that can make decisions on their own based on your specific criteria and hold interactive conversations with your users. Some use cases include feedback collection, appointment booking, and IT service request simplification.

It’s up to you to decide if you want to create an AI agent from scratch or use an existing template from the Jotform AI agent directory. Either way, you don’t have to write a single line of code to get the functionality you need. 

You have the following options to start the automation process:

• Upload your form: If you already have a form created, you can upload it to the platform and let Jotform’s AI algorithm create an assistant for you.
• Start from scratch: You can use Jotform to create a new form from scratch and then customize it as you see fit.
• Use a template: You can choose from Jotform’s extensive library of templates, which are pre-built forms designed for specific industries and use cases.

Then you simply feed URLs or documents to your AI agent to train it. You can further customize your AI agent in the Agent Builder. Here, you can determine how users will interact with your agent and set up responses based on different triggers, such as the keywords they enter.

Get started for free with Jotform AI Agents

Now that you know how to build an AI agent with ChatGPT, it’s clear that starting from scratch requires a lot of time and expertise. From API use to familiarity with programming languages, you’ll need prior knowledge to be successful.

A more straightforward and time-efficient approach is to use Jotform AI Agents. There are over 7,000 AI Agent Templates, so you’re sure to find one to suit your needs. Try it for free today.

Photo by Sanket Mishra