Generative ai vs AI agents vs Agentic AI

Summary

This video explains the progression from generative AI to AI agents and agentic AI, highlighting the increasing complexity of tasks that these systems can perform. Generative AI, based on large language models (LLMs), generates content like text, images, and videos but lacks access to real-time data. By integrating tools and APIs, an AI agent can autonomously perform tasks like booking flights. With agentic AI, multiple agents coordinate to handle more complex, multi-step goals, such as travel planning and visa processing. The video also touches on frameworks like N8N and provides examples of AI systems in action.

Takeaways

• 😀 Generative AI refers to AI that creates new content, like text, images, or videos, based on patterns learned from existing data.
• 😀 Generative AI models like GPT-4 are trained on large datasets from sources like Wikipedia, Google Books, etc., but have a knowledge cutoff date.
• 😀 A generative AI model can provide answers based on its training, but it cannot fetch real-time information unless connected to external tools or APIs.
• 😀 When an AI model is connected to APIs (like travel or weather), it becomes more intelligent and can fetch real-time data, enhancing its capabilities.
• 😀 LLM (Large Language Model) is like a brain, and by adding tools (e.g., a hammer, screwdriver), it can perform more complex tasks.
• 😀 An AI agent goes beyond simple Q&A; it performs tasks autonomously using tools, memory, and knowledge, making decisions based on the task at hand.
• 😀 AI agents can autonomously complete specific tasks, such as booking a flight, by interacting with tools and making decisions, such as finding the cheapest flight.
• 😀 When asked a complex question with multiple criteria, an AI agent can use APIs for weather, flight comparison, and more to solve the problem.
• 😀 Agentic AI is a system where multiple AI agents collaborate to solve more complex, multi-step tasks, such as booking a flight and checking visa eligibility.
• 😀 The complexity of tasks increases as we move from generative AI (basic content creation) to AI agents (task execution) to agentic AI (autonomous, multi-agent coordination).

Q & A

What is generative AI?

Generative AI refers to artificial intelligence systems that can create new content, such as text, images, or videos, based on patterns learned from existing data. It generates responses or content, like how GPT models do, by analyzing large volumes of data like books, websites, and more.

How does a large language model (LLM) work in generative AI?

A large language model (LLM) like GPT is a core part of generative AI. It is trained on vast amounts of internet data, including texts from Wikipedia, Google Books, etc., to generate responses to questions. However, it has a knowledge cutoff and cannot provide real-time information without additional resources.

What happens when a generative AI system like ChatGPT has access to external APIs?

When a generative AI like ChatGPT is given access to external APIs, such as travel services like Xedia or flight APIs, it can fetch real-time data and perform tasks beyond simple Q&A. For example, it can provide up-to-date flight prices or book tickets based on the latest information.

How is an AI agent different from a simple generative AI?

An AI agent goes beyond simple Q&A by using tools, memory, and knowledge to perform tasks autonomously. It can take actions, such as booking a flight, and make independent decisions based on the information available to it. A simple generative AI can only respond to queries.

What kind of tasks can an AI agent perform?

An AI agent can perform tasks that require reasoning and decision-making, such as booking a flight or completing complex actions using external tools. It can autonomously execute actions by accessing APIs, considering factors like budget, weather, and available resources.

What makes agentic AI different from AI agents?

Agentic AI involves a more sophisticated system where multiple AI agents work together autonomously to handle long, complex tasks. It is capable of multi-step reasoning, planning, and coordination, allowing it to tackle intricate goals, like handling travel and visa needs through different agents.

Can agentic AI systems involve only one agent?

Yes, agentic AI systems can involve a single agent. However, the complexity typically increases when multiple agents are involved, each handling different parts of a larger task, such as a travel agent coordinating with an immigration agent.

What role does autonomy play in agentic AI systems?

Autonomy in agentic AI systems refers to the ability of the agents to make decisions and act independently to complete complex tasks. These agents do not require constant supervision and can work across multiple steps or objectives, even making decisions without human input.

How do multi-step reasoning and planning work in agentic AI systems?

In agentic AI, multi-step reasoning and planning allow the system to break down a task into smaller, coordinated steps. For instance, in a travel booking system, it might first check weather conditions, then find flights, and finally check visa requirements, with each step handled by different agents working autonomously.

What tools are available to build agentic AI systems?

There are various tools available to build agentic AI systems, such as N8N, which allows the creation of workflows that integrate AI agents with tools, memory, and data. These systems can be used to build complex applications like employee onboarding or flight booking.