Intelligent agents are computer programs or systems that exhibit autonomous behaviors and decision-making capabilities. The simplest and most common examples of these agents are the thermostats used in refrigerators and air conditioners that automate temperature control and anti-malware software programs in personal computers.
However, with the advancements in the different subfields of artificial intelligence, the capabilities and applications of these intelligent agents have expanded. So-called AI agents have emerged as a subset of intelligent agents that use artificial intelligence techniques to perform tasks with minimal to zero human intervention.
What Are Agents in Artificial Intelligence? How Do They Work? What Are The Capabilities and Applications of These AI Agents?
Definition and Characteristics
The terms “intelligent agents” and “AI agents” are often used interchangeably. However, there is a difference between simpler intelligent agents and intelligent agents based on artificial intelligence. It is important to underscore the fact that there are two broad categories of intelligent agents. These are rule-based agents and learning-based agents.
Rule-based agents follow a set of predefined rules or logical statements to make decisions and take action. Learning-based agents can improve their behavior and performance by learning from experience or data. AI agents are learning-based intelligent agents. They are “smarter” than simpler intelligent agents because they can learn from their environment.
A simple thermostat automates temperature control using temperature sensors while a common anti-malware software program runs background and scheduled scanning to detect unusual activities and possible threats. These agents have some degree of independence but they are not smart nor are capable of demonstrating true intelligence.
What sets AI agents apart from simpler intelligent agents is that they are based on artificial intelligence techniques and technologies such as machine learning or deep learning and artificial neural networking to perform autonomous actions or behaviors and make decisions based on their environments, goals, and constraints.
The aforesaid means that the main characteristics of these AI agents are communication, perception, reasoning, learning, and autonomy. These agents can collect and analyze available data relevant to their functions or tasks to come up with their own processes on how to accomplish specified tasks or human-provided goals and objectives.
Furthermore, based on these characteristics, these AI-powered intelligent agents adhere to a set of rules that define their functions. These include being able to perceive their environments, making decisions based on these environmental observations, being able to make actions based on their decisions, and ensuring that their actions are rational.
Specific Types of AI Agents
There are different types of artificial intelligent agents. Some are categorized according to the relevance of their decisions and actions as far as time is concerned. General examples include reactive agents and proactive agents. Take note of the following:
• Reactive Agents: These are AI agents that respond to immediate stimuli from their environment and take actions based on these stimuli.
• Proactive Agents: These are AI agents that take initiative and plan ahead to achieve their goals. These goals are usually predefined through human interventions.
Other AI agents are classified according to the environment in which they would be placed and used. These are agents modeled and built for fixed agents and those modeled and built for dynamic agents. Take note of the following:
• Fixed-Environment Agents: They have a static set of rules that do not change. They are somewhat similar to rule-based intelligent agents but have the capabilities to perceive their immediate albeit fixed environments.
• Dynamic-Environment Agents: They are built for environments that are constantly changing and are required to adapt to new situations. They use both rule-based models and learning-based models.
AI agents are categorized according to the extent of their decision-making capabilities or the process in which they make decisions. Examples include reflex agents, model-based agents, goal-based agents, and utility-based agents. Take note of the following:
• Reflex Agents: They make decisions and actions based on current precepts using an event-condition-action rule. These decisions and actions are pre-programmed outcomes that are based on a list of pre-set of conditions.
• Model-Based Agents: These agents are similar to reflex agents but they have a more comprehensive view of their environments. The internal systems of these agents are programmed with an environmental model.
• Goal-Based Agents: They make decisions and actions based on specified goals. These decisions and actions are produced based on existing knowledge, previous experiences, newer knowledge or data, and user input.
• Utility-Based Agents: These agents are similar to goal-based agents but each of them makes a selection of decisions. A particular agent then evaluates each to choose the most optimal decision that is best suited for achieving the specified goal.
Applications and Examples
The applications of AI agents are expansive and are continuously expanding. Some notable ones include personal digital assistants for smartphones and smart devices, recommender systems used in electronic commerce websites and streaming platforms, advanced chatbots, investing and trading software programs, and game-playing agents.
Several generative artificial intelligence with self-prompting capabilities like AutoGPTs are specific examples of these agents. Advances in natural language processing and large language models such as multimodal language models have enabled these programs to generate data or content without prompting and with minimal human intervention.
Another example of an AI agent is the AlphaGo program which uses deep learning and other AI techniques to play strategic board games like Go and chess. The intelligent hardware and software management features found in iPhones and Android devices and the recommender systems used in Netflix and YouTube are other examples.
Self-driving or autonomous vehicles are also perfect examples. These vehicles use AI to navigate their environment and make driving decisions. They are equipped with computer vision for collecting data about the surroundings using sensors such as cameras and lidar and processing them using machine learning and deep learning algorithms.
Other practical applications of AI agents center on the full automation of certain tasks within specific occupations and professions. Generative AI programs can start and finish the entire content creation process or the specific phases of software development. Some agents can even replace humans in accomplishing administrative duties.
