Everything About AI Agents (Intelligent Agents): Definition, 4 Use Cases, and Limitations
What Is an AI Agent (Intelligent Agent)?
It refers to an artificial intelligence system that, without direct human operation, uses data to understand a situation, make decisions, and even take action.
How Does an AI Agent (Intelligent Agent) Differ from ChatGPT?
Let's take booking a meeting room as an example.
Situation
"Book a meeting room for 2 PM tomorrow."
LLM chatbot response
"Let me walk you through how to book a meeting room. First, log in to the meeting-room booking system and..."
AI agent(intelligent agent) response
"Sure, I've booked a meeting room for 2 PM tomorrow. Room A has been reserved, and I've sent invitation emails to the attendees. Is there anything else I can help you with?"
What Are the Characteristics of an AI Agent (Intelligent Agent)?
Ability to act
AI agent (intelligent agent): It can perform real actions (e.g., sending emails, scheduling appointments, searching files).
LLM chatbot: It mainly handles text-based conversations and cannot carry out direct actions.
Tool use
AI agent (intelligent agent): It can leverage various external tools and APIs (e.g., the Google Calendar API, visiting websites, querying databases).
LLM chatbot: It only generates responses based on its trained knowledge.
Goal orientation
AI agent (intelligent agent): It plans and executes to achieve a specific goal (e.g., booking a meeting room, closing a sales meeting).
LLM chatbot: Its primary purpose is generating responses to user questions.
Memory and state
AI agent (intelligent agent): It tracks and remembers long-term goals and the progress of tasks (e.g., taking previously set requests into account).
LLM chatbot: It maintains memory only in a limited way, within the conversation context.
How Does an AI Agent (Intelligent Agent) Work?
An AI agent (intelligent agent) basically works through three stages.
Perception : It can sense its surrounding environment through data feeds, user input, sensors, and more.
Decision-making : Based on the perceived data, the AI makes decisions.
Execution : Once a decision is made, it takes real action, such as sending an email directly.
While LLM chatbots like ChatGPT only make AI-based decisions, the difference with an AI agent is that it perceives the environment and takes action.
AI Agent (Intelligent Agent) Use Cases
We've put together some use cases of AI agents that are actually being used at work.
1️⃣ An AI agent (intelligent agent) that automatically sends emails and books appointments when a customer inquiry comes in
When a customer interested in your company's service leaves an inquiry email, you can set up an AI agent (intelligent agent) to reply instantly. It can look up the customer's LinkedIn information and send a fairly personalized email. You can also configure it to send another email after a set period if there's no reply.
2️⃣ An AI agent (intelligent agent) that summarizes the meeting agenda before a meeting
When a meeting is scheduled, you can have it send a summary of the other party's information and the meeting agenda beforehand. For example, when a meeting is added to Google Calendar, you can ask it to pull the other party's LinkedIn information and review the emails exchanged with them to organize the meeting details.
3️⃣ An AI agent (intelligent agent) that summarizes YouTube videos for you
An LLM like Claude can't handle a request to summarize when you just leave a YouTube link. That's because it can't fetch the video's subtitle information. With an AI agent, you can input a YouTube link and have it pull the subtitle information and summarize it.
4️⃣ An AI agent (intelligent agent) that visits a specific web page every day and notifies you when a new event appears
You can also have it repeatedly check a specific web page and notify you when new information appears. For example, you can build an AI agent (intelligent agent) that checks Company A's job postings every day and sends an email when a new posting is registered.
Limitations of AI Agents (Intelligent Agents): Hallucination
AI agents (intelligent agents) still have many limitations, due to the hallucination problem of LLMs. They may pretend that information that doesn't exist is real when responding to a customer, or pull in incorrect information. For this reason, building an AI agent (intelligent agent) that doesn't cause problems at a real company requires careful fine-tuning and a system of safeguards.
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