Not All AI Is the Same: Chatbots, Agents, and Beyond.
Chatbots, Virtual Assistants, Generative AI, AI Agents and Agentic AI are terms we hear frequently today. However, the difference among them can sometimes be confusing.
In this blog, we will break down each concept with simple real world examples to help you understand them clearly. By the end, you will have a clear picture of what each type of AI does and how they differ from one another.
Chatbots
Chatbots are designed to handle basic and repetitive tasks through scripted conversations.
They cannot think or reason on their own.
They answer questions or perform basic tasks based on user input or category selections.
They operate through pattern matching and decision trees.
When a chatbot receives input from a user, it scans for words or phrases that match predefined rules and then returns the corresponding response.
They work best when the range of questions is narrow and the expected responses are clearly defined.
Examples
Menu Based Chatbot - Suppose you want to find doctors within your healthcare network. You can use the healthcare website "Chat with Us" option, select the appropriate menu/category, and provide your location. The chatbot will then display a list of in-network doctors based on the information provided.
FAQ Chatbot - Company website chatbot answering questions about business hours, services, passwords reset help and policies.
Virtual Assistants
Virtual Assistants are AI-powered tools that perform tasks based on voice or text commands.
They use Natural Language Processing(NLP) and Large Language Models(LLMs) to understand user intent, context, and previous interactions.
This allows them to respond naturally, provide relevant information, or trigger actions through connected systems.
They are a perfect example of Virtual Assistants with Conversational AI.
They are more flexible than traditional chatbots.
While they are highly useful, their capabilities are generally limited to what they're programmed to do.
Examples
Smart Home Assistants - Setting alarms and reminders, controlling smart lights, checking weather updates, playing FM stations, and many other use cases of Siri and Alexa.
Smart TVs - Searching content using voice commands.
Car Assistants - Navigation, music control, reading text messages, and adjusting vehicle settings.
Workplace Assistants - Grammarly, Zoom AI, Microsoft Copilot and Outlook Copilot.
Generative AI
Generative AI generates original content such as text, images, audio, code, videos, presentations, and more based on patterns and structures learned from large amounts of training data.
Provides suggestions and content but does not execute actions like sending emails or performing tasks automatically or taking real-world human-like actions.
Prompting plays a vital role in Generative AI. When a GenAI system receives a prompt, the LLM predicts and generates responses based on patterns learned from training data.
Its answers may sometimes be outdated or inaccurate because it does not have real-time information and is trained on historical datasets.
Even trough it has many advantages, these models can sometimes produce hallucinations and biased outputs.
Examples
GitHub Copilot - Auto-complete programming scripts, explain complex code etc.
Microsoft & Outlook Copilot - Summarize emails and meeting notes, document edits, draft emails etc.
ChatGPT & Gemini - Write code, learn new topics, search for specific information and many more.
Midjourney - Allows Creators to generate high quality, artistic images using prompts.
Runway - Generates videos using creator prompts and images.
Generative Models help doctors to analyze medical scans like X-rays, MRIs and CT images to detect diseases early and track how a disease may progress.
AI Agent
AI Agents are systems that use an LLM as their reasoning engine to independently plan, use external tools, and execute multi-step workflows to achieve a specific goal.
They combine Large Language Models(LLMs), Machine Learning(ML), external tools, memory, and decision-making capabilities to perform tasks effectively.
Able to interact with multiple systems, applications, and data sources to gather information and complete tasks.
Designed to solve problems autonomously within the objectives and boundaries define by humans.
They can break down complex tasks into smaller steps, make decisions, and take actions to achieve the desired outcome.
Some AI agents can use memory and feedback to improve their performance over time.
Able to automate repetitive tasks, increase productivity, and support decision-making.
Examples
Appointment Scheduling - Automate appointment scheduling, patient registration, and bill creation.
Interview Scheduling - Automatically set up meetings with candidates and send follow-up emails .
Job Posting - Write job listings with responsibilities, required experience, and other qualifications, in line with the employer's hiring policies.
Anti-Money Laundering & Fraud Detection - Monitor transactions continuously for suspicious activity .
Automated Trading - Analyze data on market fundamentals, price movements, trading volumes, risk, and other factors to inform securities trading strategies.
Service Tickets - Learn from past ticket data to classify customers requests and better understand requests, automate responses to common queries, and escalate queries needing more attention.
Speed Up Returns - Check the policy on returned items, generate a return order, and sent it to a customer.
Agentic AI
There is a subtle difference between the terms "AI Agents" and "Agentic AI". AI Agents are the building blocks, whereas Agentic AI is the complete system that coordinates and manages those agents to achieve a goal.
Agentic AI systems have a high level of autonomy and often work by using one or more AI agents, which are autonomous entities designed to perform specific tasks.
The Key components are as follows:
Perception: Gathers context and real-time information from its environment, such as APIs, web searches, databases, documents, and other external sources.
Reasoning: Using a Large Language Model(LLM), Agentic AI analyzes the gathered information to understand the context, identify relevant details, and determine possible solutions.
For example, if the goal is to plan a vacation within a budget, the system may identify available holidays, affordable travel options, weather conditions, popular attractions, and accommodation choices by gathering information from multiple sources.
Planning: Uses the collected information to create a plan. This involves setting goals, breaking them down into smaller tasks, and determining the best way to achieve them.
Action: Based on the plan, the AI takes actions. In the vacation example, this could include searching for flights, comparing hotels, recommending places to visit, and booking reservations through connected systems.
Reflection: After taking action, the AI evaluates the results, learns from feedback and errors, and adjusts its future plans and actions to improve outcomes.
Examples
Jules(Google) - Google's coding agent that helps developers fix bugs, write tests, and update software dependencies and complete coding tasks automatically. Developers can review the results and choose the best solution.
LinkedIn Hiring Assistant - An AI agent that helps recruiters find suitable candidates, create screening questions, and draft outreach messages. It reduces the time spent on repetitive hiring tasks.
Summary
Most AI system help us automate repetitive tasks, improve productivity and make work easier. Here is the summary of what we learned so far.
Chatbots provide basic assistance through predefined conversations and are best suited for answering common questions and handling simple task.
Virtual Assistants provide more flexible assistance through voice or text interactions and can help users complete tasks using natural language.
Generative AI creates content such as text, images, code, audio and videos based on patterns learned from large amounts of training data.
AI Agents use reasoning, planning, and external tools to perform multi-step tasks and achieve specific goals with minimal human guidance.
Agentic AI combines one or more AI agents to autonomously plan, make decisions, take actions, and adapt based on feedback to accomplish complex goals.
