Why AI Agent is the next frontier for the AI field

Hello Product Folks!

GenAI was just the beginning. The future of AI is all about AI Agents!

So Why Read This Now?

This article explores AI agents and their growing importance in our future.

In short — the Future of Work is changing right now.

We'll dive into what that means and what the top minds in AI are saying about AI agents.

My goal is for you to understand AI agents, their features, and their uses.

Read this article to learn why the AI field is focusing on advanced AI agents that could change how we use artificial intelligence.

Whether you're experienced in AI or new to it, understanding the move towards AI agents is crucial for staying informed and being part of this exciting change, especially those involved in product development.

AI is moving beyond simple models to create powerful agents that can boost human intelligence in many areas. This shift to AI agents will change how we work, live, and interact with technology.

Let’s dive in…

ICYMI…

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1- What’s next for GenAI?

GenAI is only the start; the next step is AI Agent.

I spent hours on AI research papers written by Andrew Ng, Andrej Karpathy and many other AI experts.

And there is a trend from LLM to RAG to AI Agent. Here’s the pattern in the image below.

No what are tech visionaries saying?

The future of AI will involve AI Agents! In this article, we'll explain what that means. Find out what top AI experts are saying about AI Agents.

This article is a must-read for organization and product leaders who want to:

• Understand the future of work: See how AI agents will reshape industries and your product management career.

• Gain a competitive edge: Stay ahead in a rapidly evolving technological landscape.

• Be an early adopter: Learn how to create AI agents and harness their power to enhance your products.

2- Why Do We Need AI Agents When We Have LLMs & RAGs?

From a product manager angle, it's crucial to understand how things are getting built and why AI agents are becoming essential, even with the advancements of LLMs (ie Large Language Models) and RAGs (ie Retrieval-Augmented Generation).

• Goal-Oriented Behavior

LLMs and RAG models generate text based on patterns in training data, but they can't set or pursue specific goals. AI agents, however, can be designed with explicit goals, planning, and actions to achieve them. This goal-oriented behavior is vital for product managers looking to build intelligent, purposeful softwares.

• Memory and State Tracking

Most language models process each input independently without persistent memory. In contrast, AI agents can maintain an internal state, accumulating knowledge over time to inform future decisions and actions. This capability allows for more intelligent and adaptive product development.

• Interaction with the Environment

LLMs operate in the text domain without direct interaction with the physical world. AI agents can perceive and act upon their environment, be it digital or physical. For product managers, this means creating products that can interact with and respond to real-world scenarios.

• Transfer and Generalization

LLMs excel at tasks similar to their training data but struggle with entirely new domains. AI agents have the potential to learn, reason, and plan, allowing for better transfer and generalization to novel situations. This adaptability is key for product managers aiming to innovate across diverse use cases.

• Continual Learning

Once trained, most language models are static. AI agents, on the other hand, can continuously learn and adapt as they interact with new environments and situations (like humans). This ongoing learning process is crucial for developing products that evolve with user needs.

• Multi-Task Capability

LLMs are typically specialized for specific language tasks. AI agents can be general, multi-task systems, combining skills like language, reasoning, perception, and control. This versatility is beneficial for product managers dealing with complex, multi-faceted problems.

See below the illustration on the future of work.

Why AI Agents Matter?

While LLMs and RAGs have pushed the boundaries of language generation, AI agents represent a step towards more intelligent, autonomous, and multi-capable systems. They offer the ability to truly understand, learn, and solve real-world problems, making them indispensable for the future of product management.

Embrace the shift towards AI agents to enhance your products and stay ahead in the rapidly evolving tech landscape.

3- How AI Agents Will Change the World