AI Agents: How Intelligent Systems Work and Learn

Artificial Intelligence agents are computer systems that observe their surroundings, make choices, and act to reach specific goals.

According to Wikipedia, AI agents work independently. They use sensors or data to understand their surroundings, make decisions based on rules or things they’ve learned, adapt as they gain more experience, and communicate with people or other systems to reach their goals.

From chatbots to self-driving cars, AI agents help make things easier, save time, and create new tech opportunities.

TL;DR:
AI agents are intelligent systems that observe, learn, and act autonomously to achieve goals. They come in forms like reactive, model-based, goal-based, and utility-based agents—powering technologies from chatbots to self-driving cars. While they improve efficiency across industries, challenges remain, including bias, misinformation, and high computational costs. As AI agents evolve, they’re becoming more adaptable and capable of reasoning, driving smarter automation and transforming industries like healthcare, finance, and robotics.

Image credit: Walkingtree

AI agents come in different forms, with each designed for specific tasks and decision-making approaches. The common types include:

Reactive agents

Reactive agents are the simplest form of AI agents because they operate based on the current inputs they receive and do not memorize any data of past states. Their actions are determined by rules that dictate how they should respond to specific situations.

IBM's Deep Blue serves as a major example of a reactive agent. This chess-playing computer gained widespread attention in 1997 when it defeated world champion Garry Kasparov.

Model-based reflex agents

Model-based agents differ from simple reflex agents because they maintain an internal state that represents the current situation in the environment. By utilizing this internal model, they can work based on both past experiences and current perceptions. This ability allows model-based agents to handle a wider range of situations and adapt to changes in their surroundings. make decisions

Goal-based agent

These agents are crafted to pursue defined objectives. They evaluate potential actions by considering how effectively each one can lead to a desired outcome. A prime example of a goal-based agent is a sophisticated navigation system that seamlessly calculates the most efficient route to a destination. This system not only assesses the shortest distance but also factors in real-time traffic conditions, road quality, and possible obstacles, ensuring that users arrive at their destination as quickly and safely as possible.

Utility-based agents

Utility-based agents make choices based on how much they think different options will benefit them. Their aim is to get the best results by balancing different factors.

An example of a utility-based agent is Netflix’s recommendation engine. It suggests movies and TV shows to users by guessing what they will like the most. The system looks at what users enjoy, their watching history, and other things to provide recommendations that will make them happy.

AI is amazing but still has its flaws. Creating and using AI agents involves challenges that developers and organizations need to solve to make the most of them. Let’s take a closer look at the main limitations of AI agents, including the difficulties of building them and making sure they work well consistently.

Lack of true understanding

AI agents operate based on statistical patterns, not genuine comprehension. They process language and data without actual reasoning, emotions, or real-world experience. This leads to misinterpretation of complex queries, surface-level answers without deeper insights and finally difficulty in handling ambiguity or nuanced topics.

Hallucinations and misinformation

AI models can sometimes create believable but wrong information. This happens because they choose words based on likelihood instead of checking facts, don’t know what is true, and have difficulty with unusual situations. For instance, an AI might confidently give a wrong number because it came across similar wording in the data it was trained on.

High computational and energy costs

Training and using large AI models require a lot of computing power. This results in high costs for businesses, uses a lot of energy, has negative environmental effects, and focuses AI capabilities in the hands of a few big tech companies.

Ethical Use and malicious applications

AI can be used for deep fakes, misinformation campaigns, automated scams, phishing attacks, and surveillance violations, making responsible AI use a growing challenge as bad actors find new ways to exploit it.

Bias and ethical concerns

AI systems can pick up biases from the data they learn from. This can cause unfair treatment, support stereotypes, and cause ethical issues when making decisions. For example, in hiring tools powered by AI, these biases might favor certain groups of people based on past data.

Lack of generalization and adaptability

AI has a hard time with things that need common sense, thinking in a creative way, and keeping up with fast changes. This makes it tough for AI to use what it learns in one area and apply it to another without a lot of extra training.

Dependence on quality of training data

AI models depend on the data they are trained on. If the data is old, they might give wrong or useless answers. They won't perform well in specific areas if they have too little information. Also, if they mostly use popular sources, they might not show different viewpoints, creating a limited view instead of a broad one.

AI agents are improving industries by taking over tasks, helping with decisions, and improving things. They are used in many areas, such as:

1. Chatbots and virtual assistants

AI agents power chatbots and virtual assistants, enabling them to understand and respond to user inquiries in real-time. For instance, companies like Zendesk and Drift have integrated AI-powered chatbots that engage with customers, providing instant responses to common queries or directing them to human agents when necessary.

2. Autonomous vehicles

Self-driving cars like those developed by Waymo, Tesla, and Zoox utilize AI agents to process data from sensors, enabling them to navigate and make decisions autonomously. These AI systems analyze information from cameras, LIDAR, and other sensors to understand their environment and drive safely. For example, autonomous vehicles rely on AI to interpret traffic conditions and ensure passenger safety.

3. Financial and healthcare decision support

In finance, AI agents assist with tasks like fraud detection and investment management by analyzing vast datasets to identify patterns and anomalies. Similarly, in healthcare, AI agents support diagnostic processes and treatment planning. For example, IBM's Watson for Oncology uses AI to analyze medical literature and patient data to provide evidence-based treatment recommendations.

4. Robotics and Automation

AI agents are integral to robotics, enabling machines to perform tasks such as assembly line work, packaging, and quality control. In manufacturing, robots equipped with AI can adapt to new tasks and optimize production processes, enhancing efficiency and precision.

These applications show how flexible and helpful AI tools can be in different industries, leading to new ideas and better efficiency.

According to Microsoft and Google, AI agents are becoming more adaptable and capable of reasoning in complex situations. They are integrating with physical systems like robotics and IoT, allowing for smarter automation. As they improve, they will handle more autonomous decision-making, reducing the need for human intervention in critical tasks.

In the last year alone, generative AI usage among business leaders and AI decision makers jumped from 55% to 75%. New AI tools will bring even more potential.

AI agents operate autonomously, learn from experience, and assist in various fields like automation, healthcare, and finance. As they advance, they will become more integrated into daily life, making technology smarter and more responsive to human needs.