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What is an AI agent? Agentools explains after analyzing 700 AI agents
An AI agent is an AI that can not only think, but also “run and execute tasks on its own.” Whereas generative AI of the past was only able to provide answers through conversation, today’s AI agents have the ability to actually take action and complete tasks.
The reason why AI agents are attracting attention is clear. AI doesn’t just think and respond, but also produces results, so it can be applied in many ways in real business, and can directly contribute to increasing sales and improving productivity. Furthermore, while the introduction of AI was previously expensive, the advent of AI agents has dramatically reduced that cost, expanding the opportunities for introduction even for small and medium-sized enterprises.
Currently, there are many existing AI agent SaaS on the market. Because technological innovation in generative AI is so rapid, large companies that develop their own AI often find that their technology becomes obsolete midway through development. As a result, we are now in an era where it is more rational to use existing AI agent SaaS rather than developing from scratch.
Until now, IT tools have taken a long time to develop, are expensive, and their implementation has not always been successful. However, with the introduction of AI agents, that is starting to change. In this article, we will explain what kind of AI an AI agent is, which has the three qualities of being “fast, cheap, and good,” as well as its development process and future vision, examples of its use in real business, and how to actually apply it to business.
We are DevnagriAI, a company that operates Agentools, a service that analyzes over 1,000 AI agents and recommends the best AI agents for each company. If you want to use AI agents to generate new sales, reduce costs, and accelerate your business, please use Agentools to find the best tool for your company.
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The Basics of AI Agents in 3 Minutes
What are AI agents?
An AI agent is an AI system in which generative AI thinks and judges autonomously and actually executes tasks. It is easy to understand if you think of it as generative AI’s thoughts + functions (executing tasks).
For example, a normal chatbot is not called an AI agent. However, if the chatbot not only answers customer questions but also records the results and compiles and analyzes them, it becomes an AI agent.
More recently, Dr. Andrew Ng, a professor at Stanford University and founder of Google Brain, received a lot of support for his statement that an AI agent refers to anything that is agentic (acts autonomously). Furthermore, in March 2025, OpenAI published a blog post defining an agent as “an automated system that can independently perform tasks on behalf of a user.” However, in the same week, the company also published developer documentation defining an agent as “an LLM with instructions and tools” (TechCrunch 3/15 newsletter). Thus, even OpenAI, the home of generative AI, is in a state of flux, with no clear definition of the term itself.
Why are AI agents becoming so popular now?
The reason why AI agents are attracting attention is because, unlike simple AI (generative AI), they can function as “agents.” In other words, they have properties such as “being able to process tasks autonomously,” “being able to consult in natural language during the task processing process,” “being able to collaborate with other agents,” and “being able to control the physical world, including robots.” For these reasons, there are two reasons why A agents are attracting attention: expectations for the “immediate future” and expectations for the “near future.”
One is the expectation for the immediate use of AI. The functions of the “agent” mentioned at the beginning of this chapter are expected to lead to reforms in labor shortages and business efficiency. However, the reason why AI agents are attracting particular attention now is because they are like the Yoshinoya of the AI industry, being “fast, cheap, and delicious.” Until now, large companies have led the way in IT development, as it has cost at least several million to tens of millions of yen. Development periods of at least six months, and one to two years, were the norm. However, AI agents are different. More than 25,000 AI agent SAAS have already appeared, and the prices are free or several thousand to several tens of thousands of yen per month, making them affordable for individuals. You can start using them in just 10 minutes. In other words, not only large companies but even small and medium-sized enterprises and freelancers can introduce them and power up their own fighting power. And they can be used immediately. On the other hand, a major feature is that it is easy for small and medium-sized enterprises with agility to use them. For this reason, in the United States, a country advanced in AI, they are used by many small and medium-sized enterprises and freelancers. In the United States, the idea that AI agents are “fast, cheap, and good” is firmly established. The other is the expectation for the near future. It is expected that generative AI will evolve and work with multiple agents or with robots, which could bring about the biggest social change since the Industrial Revolution.
For example, what if AI could autonomously develop new drugs that are effective against incurable diseases, organize a research team, and collaborate on drug discovery research? For example, what if AI was integrated with robots and was active not only on the Internet, but also in the physical world? It is expected that AI will contribute in all aspects of society. AI agents have become popular as a marketing word.
The Beginnings of AI Agents
The concept of “AI agents” has appeared in the AI industry several times in the past, but in reality it has often ended without spreading. However, since the spring of 2024, many startups have begun to offer new AI agent SaaS (AI agent SaaS) based on generative AI.
Among them, many companies that started calling themselves “AI agents” attracted attention, and many other companies followed suit, and the term started to become popular. In other words, practical services appeared first, and then the definition of the term AI agent began to be discussed.
How have AI agents spread?
First, platforms were introduced that allowed developers to develop AI agents relatively easily, and then AI agents aimed at solving various single tasks were created one after another.
For example, Grammarly, an AI writing support service, has appeared, followed by Thoughtly, an AI phone, Clay, a GTM marketing service, and Otter, a meeting minutes tool, which have also risen to prominence and gained support. From there, AI agents have appeared in all kinds of categories, and the movement is expanding.
This movement started in the United States and has since spread to Europe and Asia. Generative AI is generally built to support multiple languages, so many of the agents are also multilingual and the services are built from the beginning, which is why it is spreading quickly around the world.
Created by developers around the world and improved daily, AI agents are a service that epitomizes the current trend, where the saying “use it, not build it” is perfectly appropriate.
AI agents have become popular because of the shared desire for "fast, cheap, and good"
The reason why AI agents have spread so rapidly is very simple: they meet the three requirements of users: fast, cheap, and efficient.
In articles written by Japanese media and AI agent development companies, it is often said that it is important to clearly set performance targets (KPIs) and to have a deep understanding of the business content. However, the essential reason why AI agents became popular in the United States is simply because they were “convenient to use.”
It’s easy to implement, inexpensive, and produces better results than doing it yourself — in other words, it’s “fast, cheap, and good.”
In the first place, no one sets KPIs when buying a PC in daily life, and no carpenter sets performance goals when buying a hammer. AI agents are used in the same way, just like casual tools.
It’s easy to try out, the implementation is quick, and there are trials ranging from a few thousand yen to a free trial, so you don’t have to worry about the cost. When you actually try it, the results are often better than you expected, which is why it’s so popular.
Nowadays, when using AI agents, it is outdated to develop original ones after detailed requirement definition and PoC (proof of concept), because there are already more than 25,000 different AI agent tools in over 20 categories.
Choose one or two that suit you best, and if they don’t suit you, try other AI agents. If you try other agents and find that they are not enough or the cost is not right, then you can consider developing your own. What you need to be careful of with original development is that AI technology evolves during the preparation period of planning, specification formulation, PoC, etc., and there is a risk that the tool you are trying to develop will become obsolete. Another concern is that original development requires you to upgrade yourself, and it is costly to keep up with the rapid evolution of AI in cycles of one to three months. AI agents are “fast, cheap, and good.” If you have time to develop your own, try an existing AI agent. AI agents are such casual and practical tools.
Understanding how AI agents work
As mentioned at the beginning, the basic structure of an AI agent is “generative AI thinking + function,” or in other words, “LLM (generative AI like ChatGPT or Gemini) + function.” For example, a reservation agent makes reservations with “LLM + browser viewing and reservation input function,” and a schedule adjustment agent makes schedule adjustments with “LLM + calendar input function.”
It is important to note that the capabilities of agents often evolve as LLMs (large-scale language models) evolve. To take advantage of this, it is necessary to use generative AI, and it is important to be able to change the model.
For example, traditional AI (AI not developed in LLM) such as IBM’s Watson is less likely to benefit from the development of generative AI, and performance improvements are generally slower. When choosing an AI agent, it is a good rule of thumb to use one based on generative AI.
By using an AI agent platform, you can often develop your own AI agent in just a day or a few days. There are also many no-code development tools available, so it’s not a bad idea to try developing one yourself.
However, when considering business applications, we recommend using an existing AI agent at first. It makes more sense to try it out and then consider developing one if you find that you absolutely need to customize it to your own needs.
If you are looking for an AI agent service that matches your company, we recommend Agentools, as it allows you to search for a service that suits your company from over 25,000 existing tools.
Types of AI Agents
Chatbot AI Agent
Chatbot-type AI agents have functions such as customer support, responding to inquiries, and automatically answering FAQs. They do not simply answer questions, but also have the ability to record information according to customer requests and trigger appropriate actions.
Marketing AI Agent
We have agents that automate ad copy generation, sales support, landing page personalization, etc. Copy AI have the ability to analyze customer data and automatically generate content tailored to individual needs.
HR AI Agent
There are three types: automatic screening of applicants and interview setting. There are agents like Moonhub that streamline the recruitment process, such as interviews, candidate acquisition, and AI-based HR and labor operations. In addition, agents that perform job evaluations based on analysis of internal communication tools have also appeared.
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SNS AI Agent
An AI agent that manages and analyzes SNS. Hootsuite can also handle things that are difficult to respond to in a timely manner through social listening.
Business automation AI agents
There are agents like Otter that automate document creation, manage tasks, analyze internal data, and create reports. These agents are particularly useful for streamlining routine tasks.
Use cases for AI agents
The US example
In the United States, AI agents are being actively used in a variety of fields, including sales, marketing, and human resources. Here are some of the most well-known examples.
New customer development and sales support
List acquisition and accuracy improvement:
Apollo: Accurate and fresh lists are essential for new customer development. Apollo is an AI agent famous for providing customer lists. It does not simply provide a list, but automatically checks the validity of the list with social media such as LinkedIn. With its large-scale list acquisition and screening function, it has become the standard service for lead acquisition during new development, known as GTM.
New customer development and sales support
List acquisition and accuracy improvement:
Apollo: Accurate and fresh lists are essential for new customer development. Apollo is an AI agent famous for providing customer lists. It does not simply provide a list, but automatically checks the validity of the list with social media such as LinkedIn. With its large-scale list acquisition and screening function, it has become the standard service for lead acquisition during new development, known as GTM.
Personalized sales:
Clay: A personalized sales approach using an AI agent is known to significantly improve response and closing rates. In a successful case using Clay, the AI automatically generated 600 dedicated landing pages for each target (when sending sales emails, landing pages were automatically created and sent to each person). The conversion rate improved significantly as detailed information about the target was reflected on the page.
A representative tool for writing and content production is Jasper, which can generate text efficiently. AI tools such as ChatGPT are also popular for writing, but Jasper makes them even easier to use. Simply enter keywords and Jasper will present a text structure proposal, and if the user makes minor adjustments, the completed text will be automatically created. It also has a function to convert text into voice messages, and is widely used in marketing and communication.
A representative HR (recruitment and scouting) tool is Moonhub, which allows for strategic recruitment. Moonhub uses AI to automatically pick up the ideal talent that companies are looking for from a database of over 1 billion people. Whether they are looking for a job or not, Moonhub proactively approaches the best talent that companies need, greatly streamlining the recruitment process. In the words of Moonhub CEO, “Just chat with AI while watching Netflix on a Friday night and you’ll have a magical experience of discovering 50 attractive candidates within 5 minutes.” In addition, it combines AI agents such as Search Agent, Converse Agent, and Handoff Agent to provide end-to-end support from interviews to recruitment.
Cases in Japan
AI agent SAAS is expensive and has inferior functions compared to other services, so we do not recommend it. It is necessary to be careful because long-established services do not use major LLMs such as chatGPT, but use their own AI.
A typical tool is Catchy, which can be used for writing and content creation.
Catchy is a long-established company in the field of AI agents in Japan, and is widely used as a tool to easily and automatically generate a variety of written content such as blogs and social media posts.
The amount of text generated is small for the price.
Sales SupportApodori: It is an AI agent tool that specializes in making appointments during sales activities. By having AI take over appointment making, sales representatives can focus on higher value-added tasks, improving the efficiency of sales activities overall.
Other AI Agents: Other AI agent tools unique to Japan have also appeared, such as “Aito” which supports customer support, and “Recruit AI” which supports human resource recruitment. Japanese-made tools are often excellent in terms of Japanese language support, but they are often more expensive than overseas tools, and cancellation is very troublesome, making them difficult to use from the user’s perspective, so caution is required.
How to deploy AI agents in your company
Should you build your own AI agent?
In conclusion, I don’t think you should build it in-house, because the AI development process generally involves the following steps:
Clarifying objectives and goals (customer service, sales efficiency, etc.)
Organizing in-house data (preparing learning data)
Measure the effectiveness with a small-scale PoC (proof of concept)
Creating a roadmap for full-scale implementation
Start of development ~ Launch
With this approach, it often takes three to six months to complete a PoC, and six months to a year to complete the product.
So, even if you spend millions of yen, in the rapidly evolving world of generative AI, it may become outdated by the time you release it (for example, ChatGPT covers all the functions without you having to develop it in-house) . To avoid such risks, use an existing AI agent. Use an existing AI agent and enjoy its effects (fast), free (cheap), and effective from the day you start using it.
How to introduce an AI agent
When introducing an AI agent, we recommend the following approach:
Discover tasks within your company that are tedious, time-consuming, and have low added value.
Find an AI agent that can do the task (or ask Agentools)
Try out the AI agent you found for a week
If it seems to be effective, introduce the paid version and spread it within the company.
If it doesn’t work, try a different tool.
Repeat this process to turn the work into an AI agent
This method takes less than a week and can be implemented at a cost of several thousand to several tens of thousands of yen. Install it before you think about it, and replace it if it doesn’t suit you. This is the appropriate stance. “Fast, cheap, and good” is the motto of AI agents.
Think about how to enjoy the benefits without spending too much time and effort on implementation. There are already tens of thousands of users of AI agents all over the world. They use AI agents as if they were creating a new table in Excel. And they are achieving results that go beyond working in Excel. The content of the work is the same in Japan and the United States. This shows that most work can be done with AI agents.
"Fast, cheap, and skilled" AI agents for your business today
AI agents are powerful business tools that are fast, cheap, and efficient. Taking the approach of “trying it out first” rather than long-term consideration is the shortcut to successful implementation of AI tools.
The benefits of implementation include:
Eliminating the labor shortage
Improved business efficiency and cost reduction
Maximizing marketing effectiveness
Creating new business models
Conventional AI tools require high initial investment and personnel with specialized knowledge, making them easier to introduce for large companies. However, AI agents can be introduced by companies of any size, both in terms of cost and personnel. For small and medium-sized companies in particular, the speed at which they can be introduced the next day at the president’s discretion is a major competitive advantage.
Let’s turn the “time spent wondering whether to introduce it” into “time for trial use.” The specific action plan is as follows:
If there are tedious tasks, consider whether they can be replaced by AI agents.
Research the best tools on sites like Ageentools
Try the recommended tools first, then decide whether they are effective
With these three simple steps, you can effectively start using AI in your business. Now is the time to use AI agents to your advantage and gain a competitive edge.
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About Agentools
Agentools is a service that recommends AI solutions that contribute to solving customer problems, with the mission of “providing the best AI agents for everyone.” It is jointly operated by Growth LLC and Devnagri AI, a leading Indian AI company (awarded the 2024 Graham Bell Prize and Tie50, among others). It provides reliable information with world-class technical capabilities and meticulous research.
Author: Murata Dai, CEO of Agentools Japan
Representative of Agentools Japan. Involved in the analysis of more than 25,000 AI agents in the world, he is passionate about supporting business transformation through technology. He aims to pursue the potential of AI agents and bring that knowledge back to the Japanese business scene. He hopes that this article will be of help to you.
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