Site Search

What are AI agents?

Background to the first year of AI agents:

An AI agent is an advanced system that combines multiple AIs and devices to autonomously perform multiple tasks or complex tasks that cannot be performed by conventional AI.

Its unique feature is that it performs tasks based on set environments and feedback, and can perform a wide range of tasks including analyzing data, assisting in decision-making, and solving problems.

 

In 2024, major tech companies announced the evolution of large-scale language models one after another. In particular, on January 6, 2025, NVIDIA CEO Jensen Huang declared at CES in Las Vegas that the era of "Agentic AI" had arrived, suggesting that AI agents could create a trillion-dollar industry, further fueling the interest in AI agents.

 

Due to a series of announcements and technological advances, 2025 is being called "the first year of AI agents."

Differences from generative AI:

AI agents and generative AI are different areas of artificial intelligence technology, each with their own unique characteristics and roles.

Purpose and Function

AI agents are specialized to achieve specific goals or execute specific tasks, and make decisions and take action autonomously.

On the other hand, generative AI is primarily focused on generating content: coming up with ideas for text, images, audio, etc.

Principle of operation and interactivity

AI agents interact bidirectionally with their environment and have the ability to continuously learn, learning from experience to improve their performance.

Generative AI is essentially a one-way process: it often just responds to user input with an output.

Output Nature

The output of AI agents is primarily actions and decisions, which are directly tied to achieving specific business goals, making it easier to measure the return on investment and quantify outcomes.

The output of generative AI is mainly content such as text, images, and audio. It is responsible for performing creative tasks and generating new information.

Benefits of Introducing AI Agents

Introducing AI agents into your enterprise has many benefits:

1. Improved operational efficiency and cost reduction

Automate routine and repetitive tasks to improve productivity.

Enables rapid response to new business processes.

Flexible operation is possible according to the size and needs of the company.

2. Enhanced data analysis and decision-making

Analyzes huge amounts of data at high speed to provide highly accurate predictions and insights.

Supports strategic decision-making based on market trends and demand forecasts.

3. Improved customer experience and added value

Chatbots and other tools are used to provide fast and accurate responses.

Deliver personalized service to improve satisfaction.

We provide an environment where employees can be freed from repetitive tasks and focus on strategic planning and creative work.

Hurdles in building AI agents

Building AI agents using local LLMs also poses several challenges.

Technical Expertise
Building AI agents requires a deep understanding of machine learning and natural language processing, and requires the use of advanced techniques such as model fine-tuning and Retrieval-Augmented Generation (RAG), so it is important to secure personnel with specialized skills.

Development Time

Customizations and integrations to meet your needs can require lengthy development times, making rapid deployment difficult, especially if they require an iterative trial and error process.

Choosing the right tools

There are many tools and libraries required to implement an AI agent, so it is important to choose the tool that best suits your company's needs.

Service Details

This is a two-month service that will accompany you from learning the basics of AI agents to implementing them according to your use cases.

Specifically, we use NVIDIA NIM to set up an inference server in an on-premise environment.

We build AI agents that can handle confidential information securely.

Sample code in Jupyter Notebook format, Q&A via email/chat, and regular meetings,

This program will teach you how to efficiently implement AI agents through lectures such as how to use NVIDIA's NIM.

 

・Month 1: Overview of AI agents / Implementing AI agents - Working with simple examples

How to use the LangGraph framework for developing AI agents

Things to be aware of when using LLM running in an on-premise environment with an AI agent

Trying out the basic ReAct (Reasoning and Acting) agent

・2nd month: AI agent implementation ~ Implementation based on use cases ~

A typical workflow for an AI agent

Design and development of AI agents tailored to the business challenges faced by our customers

Contact Us

Related article

Related product page