4 Ways to Create an AI Agent: From Coding to No-Code Solutions

An AI agent can be created in multiple ways depending on your level of experience with coding. 

Proficient coders with a good knowledge of Python can code individual parts of the AI Agent and then piece them together. Whereas, people with very little or zero coding knowledge can get their code from AI GPT tools or use no-code platforms to create AI agents. Finally, those who do not want to go through this mess can rent AI agents from platforms like VitaminAi

No matter which way you use, there are pros and cons associated with each of them, and we will discuss them at length in this article, starting with the most complex methods first, and then we will walk through simpler methods of creating AI agents.

Coding Everything From Scratch

If you are coding everything from scratch, you are going to need the following components that constitute an AI agent:

Coding Integrated Development Environment

A Trained LLM like Chatgpt, Llama, Or Flacon.

You can use an open-source Large Language Model like Llama-2, Mistral 7B, or Falcon. Using a pre-trained model for your GPT helps you create your AI agent with an already-trained AI LLM model for ready deployment.

You can add custom logic to make it application-specific. For example, you can modify the nature of output, deepening your needs. A coding AI agent needs to be highly specific, while an AI agent for romantic companionship must explore newer ways to interact with people.

A Frontend For Users

Users need a front end to interact with your model. This front end will be much like OpenAI’s Operator, where there will be a screen to see what the AI agent does and a chatbox to interact with it.

You can create a modern front-end framework like React, which is light, dynamic, and very easy to interact with.

An API to Integrate The Frontend.

The frontend of your AI agent needs to connect with the backend of your model so that when anyone enters something at the frontend, the LLM model receives the input and 

Cloud Access To Deploy The Agent.

To allow your users to interact with the AI agent, you need to deploy it as a web app on a cold platform. For your needs, you can choose from leading cloud service providers like Google Cloud, AWS, Azure, or any other cloud platform.

Benefits

1. Full ownership to copy and distribute the AI agent without having to worry about copyright issues.

2. Detailed customization is possible via this method. 

Disadvantages

1. Users are themselves responsible for the maintenance, troubleshooting, and upkeep of the agent.
2. High cost of development as it may require very large teams working for a long time before an MVP is ready.

Coding With ChatGPT or Other AI Code Generators

If you have less time coding your agent but have a sound knowledge of AI agent architecture, you can get your code from code generators like Llama Coder, GPT-4, or any other LLM model.

Benefits

1. The speed to completion is very fast.
2. The entire process can be executed by people without much coding knowledge.
3. It is much cheaper than hiring entire teams. An experienced coder can create, check, and correct mistakes, if any.
4. Little to no reliance on external people in case the agent is being created for a non-tech company.
5. There is very little risk of code compromise as only a handful of people will be working on it.

Disadvantages

1. The AI agent owner might not be able to own rights over the code as it has been generated by another code generator.
2. There could be copyright and distribution limits on the created AI agent because AI GPTs spin code from one source and present it to you.

No Code Platforms

There are multiple AI agent platforms on which you can build your agent. Some of the top platforms include Vertex by Google, Relevance AI, BubbleAI, and Voiceflow. 

Among these, we prefer Google’s Vertex agent builder because of its high uptime, and on-demand scalable resources. To use Vertex, you need Google Cloud credits.

Benefits

1. Hassle-free development. It requires little to no effort for users to create and deploy an agent in minutes.
2. The availability of all necessary components in one place, like a trained LLM, cleaned data, deployment platform, etc.
3. High-scale development of AI agents is easy with this method.

Disadvantages

1. Zero ownership as the AI agent will be a property of the no-code platform.
2. The AI agents made with these platforms will be highly generic, as user-specific customization might not be available here.
3. High cost could be one of the key disadvantages, as platforms like Google Cloud might consume large computational resources to develop generic AI agents.

The Easiest Method to Use an AI Agent

It is likely that many of us might not find any of the three methods available due to multiple reasons like complexity, the type of agent required, or the time we can spend on them. For such requirements, renting an AI agent makes far more sense than building one.

Anyone can simply visit VitaminAi, the first AI agent marketplace in the world, and use an AI agent by paying a small fee. There will also be a human in the loop to assist you with unseen problems.

All you need to do is visit VitaminAi and choose your AI agent.