From Chatbots to Autonomous Agentic AI

Introduction

From simple chatbots to sophisticated autonomous agents, Large Language Models (LLMs) have fundamentally transformed human-computer interaction. The current evolution toward Agentic AI represents a significant leap forward; unlike traditional AI agents, these systems can perform complex tasks and make independent decisions with minimal human intervention.

In this guide, we will explore the various types of AI Agents and Agentic AI and their real-world applications, making these advanced concepts accessible to anyone with a foundational understanding of Generative AI.

What is an AI Agent?

AI Agent systems are usually classified by how autonomous they are, how they reason, and how they interact with tools, memory, and other agents.

Types of Agents

  1. Reactive Agents
  2. Thinking Agents
  3. Agentic AI

Reactive Agent Workflow

Thinking and Agentic AI Workflow

1. Reactive agents

A reactive agent is a simple AI that reacts immediately to what is happening right now. It uses a set of "if-this, then-that" rules to make decisions without using past memory or complex planning.

1.1 Simple Reflex Agents

Simple Reflex Agents, it function as a purely reactive system based on the current precept and ignoring everything else including past experience and future consequences

Example:
Fix a Dr appointment on 01/02/2026
Output:
If Dr appointment is available on that day, it will fix an appointnent otherwise no action will be taken

1.2 Model-Based Reflex Agents

Uses an internal model of the world, current input and past information

Example:
Calculate Running Total
Running Total = Running Total + Current Value
Output:
Running Total

1.3 Goal-Based Agents

A goal-based agent continues choosing actions until the goal is reached or it determines the goal is unreachable.

Example:
Buy stock when reached specified price

1.4 Utility-Based Agents

A utility-based agent chooses the action that produces the best overall outcome. It compares multiple options using a scoring or reward system.

Example:
Recommendation engines
Trading bots

2. Thinking Agents

A Thinking Agent is an AI that doesn't just react—it "plans." It uses a map of its world and a memory of the past to figure out the best way to reach a goal.

2.1 Learning Agents

Learning agent learns from their experience rather than following the script

Example
AlphaGo
Personalized ad systems

2.2 Reactive Agents

Reactive Agents respond immediately to the rule based input

Example
Game NPCs
Robot Sensing Obstacles

2.3 Deliberative Agents

Goal oriented with plan and reasoning

Example
Self-Driving Car
Expert System

2.4 Hybrid Agents

Works using reactive + deliberative approaches

Example
Autonomous Vehicle
Healthcare System

3. Agentic AI

Agentic AI act autonomously, reason, plan, and execute multi-step tasks to achieve goals with minimal human intervention

3.1 Multi-Agent Systems (MAS)

Multiple intelligent agents that coordinate their actions to solve the complex problems

Example
Swarm robotics (Agriculture, Healthcare, Military and Aerial Display)
Online actions

3.2 LLM-Based Agents (Modern Agent AI)

Powered by Large Language Models,Use tools, memory, planning, and reflection

Example
AutoGPT
LangChain Agents
LangGraph workflows

3.3 Tool-Using Agents

A tool using model using LLM and use external tools autonomously to acheive the specified tasks.

Example
LLM querying SQL databases
Web-search agents

3.4 Autonomous Agents

An autonomous agent is an artificial intelligence (AI) system designed to perform complex, goal-oriented tasks independently, without the need for continuous human intervention

Example
Auto-trading bots
Auto-research agents

In this post, we explored the distinctions between AI Agents and Agentic AI, along with their real-world applications. In our next installment, we’ll get hands-on and build an LLM Agent using the Hugging Face library.

--Infinite Ripples | HK

Next Topic
Building LLM Agent using Hugging Face Library

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