Lionel Sujay Vailshery
Research expert covering cloud computing and emerging tech industries
AI agents - statistics & facts
The dawn of autonomous entities has just begun with artificial intelligence (AI) agents. These agents constitute the next step in automation technology, or simply put, “automation with intelligence.” These intelligent agents operate in their surroundings, whether physical or virtual, leveraging AI to make informed decisions and achieve specific objectives or goals through continuous learning. They utilize techniques such as machine learning and natural language processing to interact with the world, analyze data, and adapt to changing circumstances or situations. AI agents can be designed to perform a wide range of tasks, from simple automation to complex problem-solving in diverse environments including AI for robotics.
Exploring the AI agent spectrum
There are several types of AI agents, and each of these agents has their own unique approach and use-cases, interestingly, most of the AI models are focused on industrial applications. We will try to understand some of the agents with a robot-vacuum (RV) or Roomba metaphor. Goal-based agents are programmed to pursue specific outcomes, incorporated with search and planning algorithms to find optimal solutions - RV aims to clean the entire house. Utility-based agents, on the other hand, aim to maximize a utility function that represents their preferences and values, often employing decision-theoretic frameworks - RV prioritizes cleaning the dirtiest areas of the house first. Learning agents, as the name suggests, improve their performance over time through experience and feedback, typically employing reinforcement learning techniques - RV learns how to avoid furniture by learning the house layout over time.
The future is now
AI agents have a wide range of applications which are already in use across industries today. As chatbots like ChatGPT, for customer support, they can interact with customers, answering queries, and resolving issues efficiently with a hyper-personalized approach leveraging customers’ historic data and logged chats. Recommendation systems employing AI agents to analyze user preferences and suggest products or content tailored to their interests, like products on Amazon or digital content on Netflix, where traditional approaches have failed to meet customer expectations. Autonomous or self-driving vehicles like Tesla and Waymo utilize AI agents to navigate the streets and make crucial decisions for maneuvering to ensure passenger safety or optimize driving style and routes for sustainable consumption of resources.
The next step in AI evolution
AI agents are poised to become the next step of evolution of AI for businesses in our increasingly digital society, whether they are used to automating mundane tasks or optimizing complex workflows. Their capacity for learning, adapting, and making smart choices creates novel possibilities for innovation and applications in various fields.
