The term "AI agent" is appearing far more often. 2025 is even being called "the first year of AI agents," with major platformers such as OpenAI, Google, Microsoft, and Salesforce announcing AI-agent-related products one after another.

At the same time, many executives and managers may be wondering, "So what can an AI agent actually do?" and "Does our company need one?"

This article organizes the basic concept of AI agents, starting from how they differ from generative AI, then clearly explains concrete enterprise use cases, the criteria for deciding whether to adopt one, and how to get started without failing.

What Is an AI Agent?

The Basic Definition

An AI agent is an AI system that autonomously plans for a given goal, gathers the necessary information, and repeatedly makes judgments and takes actions.

In a press release in May 2025, Gartner positions AI agents as a type of "agentic AI" (Source: Gartner press release, May 14, 2025). In Gartner's framing, an AI agent is an entity that has a degree of judgment and autonomously executes part of a simple task, while the more advanced agentic AI is distinguished as an entity that possesses the capabilities of memory, planning, and tool use and autonomously carries out complex tasks.

Most of what is currently used in business is the former—AI agents—that is, ones that autonomously carry out a specific business process.

The Difference Between Generative AI and AI Agents

The biggest difference between generative AI (conversational AI like ChatGPT) and an AI agent is whether it is "instruction-waiting" or "autonomously acting."

Generative AI returns a single response each time a human enters a prompt (an instruction). To trigger the next action, a human must give an instruction again. It is, so to speak, an "excellent advisor."

An AI agent, once given a goal at the outset, thinks through the steps needed to achieve it on its own and executes multiple tasks in succession while accessing external tools and databases. It is, so to speak, an "executor that moves on its own."

For example, if you instruct it to "prepare a proposal for next week's meeting," generative AI can only go as far as generating the text of the proposal. An AI agent will autonomously advance a whole sequence of work—researching the customer's website, retrieving past meeting histories from the CRM, creating a proposal informed by that information, and sending an email to a supervisor requesting a review.

The Four Components of an AI Agent

An AI agent operates by repeating the following four steps.

Planning. It breaks down the tasks needed to achieve the goal and decides the order of execution.

Perception. It obtains the necessary information from external databases, APIs, websites, and so on. In some cases it uses a RAG mechanism to search internal documents.

Reasoning. Based on the gathered information, it judges which action to take next.

Action. It carries out actual actions, such as sending emails, creating files, and entering data into systems.

The hallmark of an AI agent is that it autonomously repeats this "planning → perception → reasoning → action" loop until the goal is achieved.

Enterprise Use Cases for AI Agents

AI agents are beginning to be used in a variety of business domains. Here we organize representative usage patterns by business area.

Customer Support

Beyond analyzing the content of customer inquiries and searching FAQ databases and past response histories to generate answers, it automatically executes even changes to order information and the issuing of tickets as needed. Whereas conventional chatbots only returned fixed answers, an AI agent differs in that it understands the context of an inquiry and can solve problems across multiple systems.

Sales Support

An AI agent automatically performs pre-meeting research (checking the customer company's website, analyzing IR information, checking the latest news) and creates a summary report. By combining this with past CRM data, it becomes possible to present even "the points of change since the last meeting" and "the points you should propose."

Internal Knowledge Utilization

An AI agent searches across internal documents, manuals, and past response records and answers employees' questions with sources. Going beyond mere document search, it is capable of autonomous behavior such as automatically updating FAQs based on its answers and escalating unresolved questions to the person in charge. This use is directly tied to the challenges of eliminating key-person dependency and passing on knowledge (for details, see our articles on How to Eliminate Key-Person Dependency and How to Pass On Tacit Knowledge with AI).

Back-Office Operations

An AI agent takes over high-volume operations that can be processed on a rule basis, such as checking and approving expense reports, matching invoices, and entering and organizing data. The difference from conventional RPA is that it can also process non-standard data (handwritten receipts, invoices with inconsistent formats) using the capabilities of generative AI.

Information Gathering and Analysis

An AI agent automates work that periodically gathers and organizes information, such as market research, competitive analysis, and checking for regulatory changes. It can execute a whole workflow—gathering information on the web, summarizing it, and notifying the person in charge according to importance—without human intervention.

Cases Where AI Agents Are Not a Good Fit

An AI agent is not a panacea for every kind of work. In the following cases, a different means is more appropriate than adopting an AI agent.

When business processes are not standardized. An AI agent is a mechanism that "moves autonomously when given a goal," but if the procedures and rules of the work are not clear in the first place, the AI cannot operate correctly either. Visualizing and standardizing the business process comes first.

Areas where strict accountability for judgments is required. In work that carries a responsibility to clearly explain the basis for a judgment—such as legal judgments, medical decisions, and personnel evaluations—it is not appropriate to leave matters to an AI agent's autonomous judgment. An AI's reasoning process tends to be a "black box," making it difficult to fulfill accountability.

When there is too little data. An AI agent's performance depends on the quality and quantity of the data it can access. If internal documents and work records are not digitized, or are extremely scarce, you will not get sufficient results even by adopting an AI agent.

In such cases, we recommend starting with a RAG-based internal chatbot (a simple system that searches internal documents and answers employees' questions). Establishing the use of internal knowledge with a chatbot, and then expanding to an AI agent once more advanced autonomous processing becomes necessary, is a realistic approach that keeps the risk of failure down.

Choosing Among RPA, Chatbots, and AI Agents

AI agents are not the only means of business automation. RPA, chatbots, and AI agents each have different strengths, so you need to use them according to the challenge.

RPA (Robotic Process Automation) is suited to automating fixed, routine tasks whose procedures are completely set. It targets rule-based work with few exceptions, such as "every morning at 9, download data from this system and paste it into this Excel file." It shines in situations where no judgment is needed and the requirement is to accurately repeat a set operation.

A RAG chatbot is a mechanism that answers questions based on internal documents and past response histories. It is suited to knowledge search such as "What is the scope of application of this rule?" or "How did we respond when there was a similar complaint in the past?" It takes a conversational form in which a human asks and the AI answers, and it does not include executing actions.

An AI agent encompasses the above two functions while performing autonomous judgment and multi-step execution. Its hallmark is combining routine processing (RPA-like behavior) and knowledge search (chatbot-like behavior) to achieve a goal with minimal human instruction.

The best option differs depending on where your own challenges lie. If "the procedures are set but there is a lot of manual work," RPA; if "you want to search internal knowledge," a RAG chatbot; if "you want autonomous processing across multiple steps," an AI agent is the right fit.

Four Steps to Adopting an AI Agent

Step 1: Identify the Work You Want to Automate

Rather than starting from "we want to bring in an AI agent," work backward from "which work do you want to automate." Enumerate candidate tasks and prioritize those that meet the three conditions of "labor-intensive," "error-prone," and "high frequency."

Step 2: Set a Realistic Scope

Rather than trying to automate the entire operation from the start, begin by entrusting the AI agent with a portion of the work (for example, only "information gathering" or only "drafting"). Starting small, confirming the effect, and gradually expanding the scope is the standard playbook for adopting an AI agent.

Step 3: Prepare Data and Systems

For an AI agent to operate correctly, the data sources it should access (internal documents, CRM, email, chat logs, etc.) need to be organized and accessible via APIs and connectors. When the digitization and organization of data is insufficient, this preparation takes the most time.

Step 4: Verify, Monitor, and Improve

In the initial stage, always establish a "human-in-the-loop" structure in which humans check the AI agent's output. You also need to design a fallback (handover to a human) for when the AI agent makes a wrong judgment. Once operations have stabilized, gradually reduce the human-check steps.

Points to Note When Adopting an AI Agent

Security and Access Permissions

An AI agent accesses internal confidential information and customer data in order to carry out its work. You need to rigorously design access permissions for which data it is allowed to access and which operations it may execute autonomously. Especially when handling customer data and personal information, measures against the risk of information leakage are essential.

Hallucinations and Malfunctions

The LLM (large language model) that forms the foundation of an AI agent carries the risk of hallucination—generating information that differs from fact. The broader the range an AI agent executes autonomously, the greater the risk of actions based on mistaken judgments. For important judgments and actions that involve sending information externally, we recommend inserting a human approval step.

Cost and Cost-Effectiveness

Because AI agents make many generative-AI API calls, operating costs tend to be high. Since completing a single task involves multiple API calls, you need to estimate the balance between usage frequency and cost in advance.

Summary

An AI agent is an AI system that autonomously plans, judges, and executes toward a goal, achieving the automation of composite work that conventional generative AI and RPA could not fully handle.

However, not every company needs an AI agent. When business processes are not standardized, or when the digitization of internal data is insufficient, the first priority is to establish the use of internal knowledge with a RAG chatbot.

When considering adoption, working backward from "which work do you want to automate" and starting with a small scope before expanding gradually is a realistic way to minimize the risk of failure.

At Teraverse, we provide generative AI solution development tailored to a company's challenges, from RAG chatbots for internal knowledge search to AI agents that integrate with multiple business systems. If you are considering adopting an AI agent, please feel free to contact us.