You may have been seeing the term “AI agent” more often and wondered how exactly it differs from the generative AI you use every day.

The difference between the two boils down to a single point: whether they can autonomously execute tasks until a goal is achieved.

If you decide to introduce them without understanding this difference, you risk making misguided internal proposals or unnecessary investments.

This article explains the differences and mechanisms of generative AI and AI agents, criteria for choosing between them, use cases, and risks associated with implementation.

By the time you finish reading, you will be able to confidently decide which one to apply to which operation in your own organization.

The difference between generative AI and AI agents is the presence or absence of autonomy

The difference between generative AI and AI agents can be summed up in one point: whether they can act autonomously to achieve a goal.

As a starting point for understanding this difference, let’s first compare their basic behaviors.

Generative AI is passive AI that responds to instructions

Generative AI is a technology that returns a single, one-off response each time the user provides a prompt.

The large language model (LLM) at the core of generative AI works by predicting and outputting the continuation of input text. It never initiates action on its own without an instruction.

For example, if you ask ChatGPT or Gemini to “summarize this report,” it will return a summary but will not proceed to submit or share it. It is a passive entity waiting for the next instruction.

Understanding this passive nature makes it easier to determine what you can and cannot entrust to generative AI.

AI agents are proactive AI that act autonomously until the goal is achieved

When given a goal, an AI agent breaks down the task on its own and continues to execute multiple steps without human instruction.

This is because it is equipped with a mechanism that uses generative AI internally as a reasoning engine, while making its own decisions about the next action to take.

For instance, if you ask it to “prepare materials for next week’s meeting,” it will autonomously search for necessary information, create the materials, and even share them with the relevant people.

Understanding this proactive nature allows you to assign tasks that generative AI alone cannot fully handle.

The mechanism of AI agents consists of four steps

The mechanism by which AI agents operate autonomously can be broken down into four steps: perception, reasoning, action, and learning.

・Perception: Recognize the goal and information
・Reasoning: Formulate a plan to achieve the goal
・Action: Execute tasks using tools
・Learning: Reflect on the results and improve

Understanding the role of each step gives you a concrete picture of what and how much an AI agent accomplishes automatically.

Perception: Recognize the goal and information

Perception is the first step in which the AI agent recognizes the goal and its surrounding information.

It not only reads the user’s instructions but also gathers information from internal databases and external APIs, assembling the material needed for the next step: reasoning.

For example, if instructed to “create a proposal document for next month based on last month’s sales data,” it will first identify where the sales data resides during the perception phase.

The more accurately it captures the necessary information at this stage, the higher the precision of the subsequent reasoning and action will be.

Reasoning: Formulate a plan to achieve the goal

Reasoning is the step where the AI agent breaks down the tasks required to achieve the goal based on the perceived information and devises an execution plan.

The AI agent internally uses generative AI (LLM) as a reasoning engine to determine what work should be done and in what order.

In the previous example, it might decompose the goal into three tasks—analyze sales data, summarize trends, and structurethe proposal document—and decide the order of execution.

This planning is the central mechanism that provides the autonomy that standalone generative AI lacks.

Action: Execute tasks using tools

Action is the step where the AI agent actually carries out tasks by coordinating with external tools and APIs according to the plan.

AI agents operate by combining not only their own generative capabilities but also search tools, file operations, and integration functions with internal systems.

In the earlier example, it would retrieve sales information from the database, compile the analysis results, and operate document creation tools to generate the slides.

Because it proceeds autonomously to this stage, it can significantly reduce the manual steps that humans would otherwise have to perform.

Learning: Reflect on the results and improve

Learning is the step where the AI agent reflects on the outcome of task execution and incorporates what did not work well into future actions.

It compares the execution results with the expected outcomes, analyzes the causes of any discrepancies, and adjusts its future judgments and actions accordingly.

If the structure of the created proposal did not match the intent, it might adjust how it prioritizes information the next time.

This feedback loop means that the more an AI agent is used, the closer its actions align with the business requirements.

Differences among generative AI, AI agents, and agentic AI

“Agentic AI” is a term that is often confused with “AI agent.”

Here, we will organize the three terms—generative AI, AI agents, and agentic AI—according to the following three points.

・Generative AI: a technology that merely responds to prompts
・AI agents: execution systems with partial autonomy
・Agentic AI: autonomous AI with memory and planning capabilities

Keeping this classification in mind will help you avoid being confused by the terminology used in articles and business discussions. Let’s first check the relationship between the three terms in a comparison table.

Generative AI: a technology that merely responds to prompts

Generative AI is simply a technology that generates content in response to a prompt on a one-off basis.

It does not have the capacity for autonomous judgment or for continuously executing multiple tasks. It is a mechanism that only returns outputs for inputs.

This category includes GPT-based text generation models, image generation models, and the like—each producing new output every time an instruction is given.

Understanding this positioning serves as a starting point when explaining how it differs from AI agents.

AI agents: execution systems with partial autonomy

An AI agent is a system that leverages generative AI as a reasoning engine to autonomously execute tasks within a limited scope.

While it acts autonomously within the boundaries of a given goal, in many cases it may not be equipped with the ability to set its own goals or accumulate learning over the long term.

Typical examples are tools that operate autonomously within a predefined scope of work, such as handling inquiries or creating documents.

Based on this characteristic, AI agents at this level are the most accessible for enterprises to adopt at present.

Agentic AI: autonomous AI with memory and planning capabilities

Agentic AI refers to AI that possesses long-term memory and planning abilities and exhibits a more advanced form of autonomous behavior.

Research and advisory firms like Gartner position it as a next-generation form with higher autonomy and goal orientation than existing AI agents.

The envisioned scenario is one where it remembers circumstances across multiple projects and adjusts its own goals and priorities in response to changing situations.

Knowing about this advanced form makes it easier to see the direction in which AI agents are likely to evolve.

Criteria for deciding whether to use an AI agent or generative AI

Once you understand the differences, you can decide which to use in practice based on the complexity of the task. The criteria are the following three.

・For one-off text or image creation, generative AI alone is sufficient.
・For multi-step business processes, AI agents are more suitable.
・A hybrid approach that combines both is also effective.

Read on while checking which category your own operations fall into.

For one-off text or image creation, generative AI alone is sufficient

Tasks that can be completed with a single instruction, such as drafting an email or generating an image, can be fully handled by generative AI on its own.

When a task completes in a single step, the planning and multi-tool coordination features of an AI agent are unnecessary.

Tasks like summarizing meeting minutes or creating a single image for a social media post can be accomplished simply by giving a direct prompt to generative AI.

Forcing the introduction of an AI agent for such tasks may actually increase operational costs.

For multi-step business processes, AI agents are more suitable

AI agents are suited to operations that span multiple steps, from information gathering and document creation to sharing with stakeholders.

Because judgment and information hand-off between steps are required, manually issuing a prompt to generative AI each time would be too cumbersome.

A typical example would be receiving a customer inquiry, checking past correspondence history, drafting a response, and, if necessary, handing it over to the person in charge.

Being able to make this distinction allows you to accurately prioritize which operations to automate.

A hybrid approach that combines both is also effective

Another effective approach is to have generative AI create a draft, and then have the AI agent handle the subsequent execution and adjustments.

This method leverages the strengths of each: generative AI’s creative output and AI agents’ execution across multiple steps.
For instance, generative AI could create the framework of a proposal, while the AI agent handles collecting relevant data and sharing it with stakeholders.

Knowing this combination allows you to extend the scope of automation while making the most of your existing generative AI implementations.

Use cases of AI agents by industry

AI agents are being incorporated into operations in different ways depending on the industry. Here we present examples from three sectors.

・Customer support: automating inquiry handling
・Sales: automating lead management and proposal creation
・Back office: automating accounting and general affairs tasks

If any of these examples are close to your own operations, use them as a reference to visualize implementation.

Customer support: automating inquiry handling

In customer support, there is a growing number of cases where AI agents autonomously handle inquiries.

They can reference past correspondence and internal knowledge bases to formulate answers, and escalate only the more difficult cases to human staff.

To be created by a human: Please insert a specific implementation case study (company name, performance figures, source URL) verified from primary sources, whether from your own company or publicly available.]

This frees up personnel from routine inquiry handling, allowing them to devote time to consultations that only humans can address.

Sales: automating lead management and proposal creation

In sales, there are cases where AI agents support everything from organizing prospect information to creating draft proposals.

They can sort through business negotiation data and past interactions, prioritize high-potential leads, and even produce an initial draft of a proposal.

[To be created by a human: Please insert a specific implementation case study (company name, performance figures, source URL) verified from primary sources, whether from your own company or publicly available.]

This reduces time spent on document creation and increases the time sales representatives can focus on the negotiations themselves.

Back office: automating accounting and general affairs tasks

In back-office areas, there are cases where AI agents automate tasks such as invoice processing and attendance management.

These are routine yet cross multiple systems, making them hard to handle with simple rule-based automation alone.

[To be created by a human: Please insert a specific implementation case study (company name, performance figures, source URL) verified from primary sources, whether from your own company or publicly available.]

Reducing verification work that spans multiple systems allows staff to be redirected to tasks that require judgment.

Risks to note when introducing AI agents

Because AI agents act autonomously, they come with new risks not present with standalone generative AI. The three main points to keep in mind before implementation are:

・Risk of information leakage due to incorrect permission settings
・Risk of erroneous operations or runaway behavior due to autonomous execution
・Risk that hallucinations from generative AI are reflected in actions

By understanding these risks before implementation, you can prevent unexpected troubles.

Risk of information leakage due to incorrect permission settings

Granting excessive permissions to an AI agent can lead to unintended access to information and data leaks.

Because the agent autonomously accesses multiple systems, if access scopes are not restricted, it may potentially reach information it should not touch.

A conceivable scenario is that an AI agent designed for customer support is also given access rights to personnel information, leading to unintentional viewing or output of that data.

You can greatly reduce this risk by configuring detailed access permissions for each business scope.

Risk of erroneous operations or runaway behavior due to autonomous execution

The nature of continuing to execute tasks without human confirmation carries the risk that processing proceeds based on incorrect judgments.

A single error in judgment can become the premise for the next action, causing the impact to accumulate without anyone noticing the mistake along the way.

For example, it might extract target data under the wrong conditions and then proceed all the way to analysis and document creation based on that incorrect data.

You can mitigate this risk by designing processes so that human approval is required at steps involving critical decisions.

Risk that hallucinations from generative AI are reflected in actions

If the generative AI that powers the AI agent’s reasoning generates incorrect information, there is a risk that the error will directly translate into actual actions.

Generative AI has a phenomenon known as “hallucination,” where it plausibly produces content that is factually incorrect, and AI agents are also affected by this.

For instance, it might create a plan based on non-existent data and proceed to prepare documents or even contact external parties based on incorrect content.

Keeping a final human check at steps that involve important decisions or external communication is an effective way to control this risk.

Main tools used for AI agent development

When actually building AI agents, specialized tools and frameworks are used. The following four are representative.

・LangChain
・Dify
・n8n
・Claude Agent SDK

Keep these in mind as options when considering in-house development or outsourcing.

LangChain: a leading framework for agent development

LangChain is an open-source framework for developing applications that use LLMs.
It provides pre-built components for functions needed in AI agent development, such as linking multiple tools and managing memory.

Engineers can build agents for tasks like inquiry handling or information gathering simply by combining these components, without having to build everything from scratch.

For engineers with development experience, this is an option that allows building full-scale AI agents in a relatively short time.

Dify: a no-code platform for building agents

Dify is a platform that allows you to construct AI agents through a GUI, even without programming knowledge.
Since workflows can be assembled simply through on-screen settings, the barrier to entry is low even for non-engineers.

You can, for example, build an AI agent that handles inquiries by importing internal FAQ data—all through screen operations.

This enables departments without specialist engineers to start using AI agents on a small scale.

n8n: a tool that combines workflow automation with agents

n8n is a tool that allows you to incorporate AI agent functions into existing workflow automation.

It is originally an automation tool that connects multiple systems, designed to allow the addition of AI-based decision-making capabilities.

For instance, when order data arrives, it can analyze the content and automatically create notification messages tailored to the relevant department according to conditions.

This allows you to gradually incorporate the decision-making capabilities of AI agents without drastically altering existing business flows.

Claude Agent SDK: an agent development kit from Anthropic

Claude Agent SDK is an official SDK for developing AI agents provided by Anthropic.
It provides a mechanism to build agents that integrate external tools and multi-step execution, using the company’s generative AI, Claude, as the reasoning engine.

It is used to develop agents that link to internal systems and autonomously carry out multi-step tasks such as code reviews and document creation.

Because it is an official SDK provided by the developer, it is a reliable choice in terms of ease of keeping up with specification changes.

Frequently asked questions about AI agents

Here are three common questions regarding AI agents and generative AI:

・What is the difference between an AI agent and ChatGPT?
・Can small and medium-sized enterprises also adopt AI agents?
・How long does it take to implement an AI agent?

Reviewing the answers to these questions will help you consider your own organization’s utilization policy.

Leverage generative AI and AI agents correctly to improve operational efficiency

The difference between generative AI and AI agents lies in whether they can act autonomously to achieve a goal. Generative AI is suited for one-off tasks, while AI agents are suited for multi-step operations. Combining them depending on the situation can also be effective.

Once you understand the difference, try categorizing your organization’s tasks into single-step tasks and multi-step processes. This will give you a concrete picture of which type of work suits which approach.

After you solidify your policy on when to use each, the next practical challenge will be deciding which tool to choose and which operation to start a trial with.

First, pick one task with a small scope of impact, and start by testing it on a small scale using a no-code tool.

Follow us on Facebook for updates and exclusive content! Click here: Maga Techs