Gen AI vs AI Agents vs Agentic AI: A Simple Guide

Generative AI

Generative AI is a kind of Artificial Intelligence that can generate the new content. It learns patterns using large volumes of data, either available on the internet or data that we feed it, and then produces text, images, audio, video, or even code.

The Generative AI systems, which are most commonly implemented, are based on the LLM (Large Language Model). The examples are ChatGPT, Gemini, Claude, or Perplexity. These models are trained on massive datasets and provide the answers depending on the information they have learnt.

In its simplest form, generative AI is reactive. It only reacts to your input, It does not think in advance, It does not have any actual memory, unless it is made to store context.

Example: Write emails, summarize documents, generate content, generate images and voiceovers, and many others. When you query a gen AI model by asking, "What is the weather today", it cannot provide the answer unless and until it is linked to the live data.

Advantages of using Gen AI

Content Creation: write articles, social media posts, emails
Automating Repetitive Tasks: Auto-generate product descriptions, summarizing documents, generating templates.
Multimodal Capabilities: Create logos, marketing visuals, generate voiceovers, podcasts
Boosting Productivity: Speed up the creative process, better decision-making

Recent Enhancements

• The most significant change is the introduction of memory. More recent models, such as ChatGPT and other models, are able to both recall your preferences and context throughout communication.
• Models can now work with large quantities of information simultaneously, think hundreds of pages of documents as opposed to a few pages.

AI Agent

AI Agent is a program that accepts the input, thinks, and performs an action to finish a task with the help of tools, memory, and knowledge. Unlike pure Generative AI, which only reacts with an answer, an AI Agent can actually do something with the information it generates. It is more independent and has some autonomy to make decisions. Usually, AI agents are designed to perform narrow, simple, and specific tasks effectively.

Once you create the LLM for your use case and give it access to external APIs or tools, your LLM is now smart enough to take action. For example, it can call the flight API and fetch the latest price of the ticket.

Let's say your LLM is not able to provide the response for the particular input, it will keep on looking for the external things that will be able to handle this particular case like – what is the weather today? 

Think of it as A personal assistant who doesn’t just tell you “Flights are available” but actually goes and books one for you.

Advantages of AI Agents

Works Automatically: You only need to give it a task and it takes care of all the steps without you having to guide it every time.
Uses Tools and Apps: It is able to integrate with other applications, search the Internet, do data analysis, and manage programs to achieve tasks.
Saves Time: It could be available 24/7, so you do not have to do repetitive or multi-step tasks.
Remembers the Task Context: It maintains a record of its actions while doing the task, and therefore does not lose its way. 

Limitations of AI Agents

Can Be Slow and Costly: Since it involves heavy processing with AI, it may take time and may be expensive to execute.
Makes Mistakes Sometimes: In the case of misunderstanding your purpose or the tool response, it could retrieve the incorrect information such as displaying you the prices of wrong date or leave a part of an answer.
Security Risks: it has access to other tools and data, there is a risk unless it is closely monitored.
Hard to Debug: When things go wrong it may be difficult to tell where and why it went wrong.

Real-World Examples AI Agents

Research Helper: Does the research work online and summarizes it on your behalf.
Data Assistant: It gathers, cleans and examines data automatically.
Travel Serch: Finds you the perfect flight and hotel deals.
Customer Support Agent: Reads the messages of the customers, interprets the problem, and forwards the request to the corresponding department.

Recent Improvements

Teamwork Features: New tools like AutoGen let multiple agents work together like a team.
Better Decision-Making: Agents are getting better at thinking through tasks and checking their own work.
Self-Correction: Some agents can now notice their own mistakes and try to fix them before finishing a task.

Tools for Building AI Agents

 Zapier , N8N, LangChain , AutoGen , CrewAI

Tools AI Agents Can Use

AI agents often rely on other tools to do their jobs. Here are a few examples:

Web Browsers: To look up real-time information (e.g., weather, news, prices).
Calendars: To schedule meetings or check availability.

Databases/APIs: To fetch or update data (e.g., pulling customer info from Salesforce).
Code Interpreters: To run Python scripts for data analysis or file processing.
Email/Chat Apps: To send messages or notifications.

Agentic AI –

The next stage of AI Agents is Agentic AI. Agentic AI is an AI system that can make decisions without human intervention and can take actions on its own to achieve a goal without being told exactly what to do at every step. AI Agents take actions when instructed to do something. Whereas Agentic AI will act on its own, it is independent, thinks in advance, and acts proactively. It is not waiting to be told what to do, it can even know what you need.

In the Agentic AI, Multiple AI Agents will be there, and they will be collaborating with each other to complete the requirement.

Advantages of Agentic AI

Teamwork: Several agents collaborate with each other and each one of them takes a portion of the task.
Greater Accuracy: Agents check the work of each other and minimize mistakes and hallucinations. 

Solves Complex Problems: Excellent in complex, multi-step problems such as making a detailed trip plan including booking the tickets, book hotels and related places to visit at the location or handling large data sets.
Flexibility: The agents are able to focus on various specializations (finance, travel, data) and merge their expertise.

Limitations of Agentic AI

Costly: More computing power and resources are required as many agents are being employed.
Hard to Implement: It is more difficult to design and coordinate a team of agents compared to when using one AI.
Slowness: Overkill, Simple tasks and Simple overweight projects.
Requires Supervision: Human monitoring is required to make sure that the team is working in a proper and safe manner.

Real-World Examples of Agentic AI

Trip Planning: A team of agents works together one finds flights, one books hotels, one creates an itinerary, and another checks for errors or better options.
Business Reports: One agent can gather data for you, another agent will analyze the data, another agent can create visuals, and the last agent can write the summary for you.
Software Development: One agent will write the code, another tests it, and a third reviews it for bugs or improvements.
Customer Support: It can look up your order, issue a refund, schedule a pickup, and confirm everything via email - all autonomously.

Tools for Building Agentic AI

CrewAI: good for creating teams of role-based agents (eg: researcher, writer) that collaborate on complex tasks.
AutoGen: Microsoft's framework for building custom groups of conversational agents that talk to each other to solve problems.
LangChain: Helps developers build multiple applications by connecting LLMs with external data sources and tools, enabling multi-step reasoning.
n8n: Workflow automation platform that lets you visually connect AI models with business apps (like CRM, email, databases) to create automated agents.

Recent News 

Recently, ChatGPT introduced the ChatGPT Agent Builder, enabling users to create agents.

Happy Exploring! Happy Learning!