Agents in AI

An AI agent is a software program that can interact with its surroundings, gather information, and use that information to complete tasks on its own to achieve goals set by humans.

• For instance, an AI agent on an online shopping platform can recommend products, answer customer questions, and process orders. If agent needs more information, it can ask users for additional details.
• AI agents employ advanced natural language processing and machine learning techniques to understand user input, interact step-by-step, and use external tools when needed for accurate responses.
• Common AI Agent Applications are software development and IT automation, coding tools, chat assistants, and online shopping platforms.

How do AI Agents Work?

AI agents follow a structured process to perceive, analyze, decide, and act within their environment. Here’s an overview of how AI agents operate:

1. Collecting Information (Perceiving the Environment)

AI agents gather information from their surroundings through various means:

• Sensors: For example, a self-driving car uses cameras and radar to detect objects.
• User Input: Chatbots read text or listen to voice commands.
• Databases & Documents: Virtual assistants search records or knowledge bases for relevant data.

2. Processing Information & Making Decisions

After gathering data, AI agents analyze it and decide what to do next. Some agents rely on pre-set rules, while others utilize machine learning to predict the best course of action. Advanced agents may also use retrieval-augmented generation (RAG) to access external databases for more accurate responses.

3. Taking Action (Performing Tasks)

Once an agent makes a decision, it performs the required task, such as:

• Answering a customer query in a chatbot.
• Controlling a device, like a smart assistant turning off lights.
• Running automated tasks, such as processing orders on an online store.

4. Learning & Improving Over Time

Some AI agents can learn from past experiences to improve their responses. This self-learning process, often referred to as reinforcement learning, allows agents to refine their behavior over time. For example, a recommendation system on a streaming platform learns users' preferences and suggests content accordingly.

Architecture of AI Agents

The architecture of AI agents serves as the blueprint for how they function.

There are four main components in an AI agent’s architecture:

• Profiling Module: This module helps the agent understand its role and purpose. It gathers information from the environment to form perceptions.
  Example: A self-driving car uses sensors and cameras to detect obstacles.
• Memory Module: The memory module enables the agent to store and retrieve past experiences. This helps the agent learn from prior actions and improve over time.
  Example: A chatbot remembers past conversations to give better responses.
• Planning Module: This module is responsible for decision-making. It evaluates situations, weighs alternatives, and selects the most effective course of action.
  Example: A chess-playing AI plans its moves based on future possibilities.
• Action Module: The action module executes the decisions made by the planning module in the real world. It translates decisions into real-world actions.
  Example: A robot vacuum moves to clean a designated area after detecting dirt.

AI Agent Classification

An agent is a system designed to perceive its environment, make decisions, and take actions to achieve specific goals. Agents operate autonomously, without direct human control, and can be classified based on their behavior, environment, and number of interacting agents.

• Reactive Agents respond to immediate stimuli in their environment, making decisions based on current conditions without planning ahead.
• Proactive Agents take initiative, planning actions to achieve long-term goals by anticipating future conditions.

• Fixed Environments have stable rules and conditions, allowing agents to act based on static knowledge.
• Dynamic Environments are constantly changing, requiring agents to adapt and respond to new situations in real-time.

• Single-Agent Systems involve one agent working independently to solve a problem or achieve a goal.
• Multi-Agent Systems involve multiple agents that collaborate, communicate, and coordinate to achieve a shared objective.

• Rational agent is one that chooses actions based on the goal of achieving the best possible outcome, considering both past and present information.

Key Components of an AI System

• An AI system includes the agent, which perceives the environment through sensors and acts using actuators, and the environment, in which it operates.
• AI agents are essential in fields like robotics, gaming, and intelligent systems, where they use various techniques such as machine learning to enhance decision-making and adaptability.

Structure of an AI Agent

The structure of an AI agent is composed of two key components: Architecture and Agent Program. Understanding these components is essential to grasp how intelligent agents function. 

1. Architecture

Architecture refers to the underlying hardware or system on which the agent operates. It is the "machinery" that enables the agent to perceive and act within its environment. Examples of architecture include devices equipped with sensors and actuators, such as a robotic car, camera, or a PC. These physical components enable the agent to gather sensory input and execute actions in the world.

2. Agent Program

Agent Program is the software component that defines the agent's behavior. It implements the agent function, which is a mapping from the agent's percept sequence (the history of all perceptions it has gathered so far) to its actions. The agent function determines how the agent will respond to different inputs it receives from its environment.

Agent = Architecture + Agent Program

The overall structure of an AI agent can be understood as a combination of both the architecture and the agent program. The architecture provides the physical infrastructure, while the agent program dictates the decision-making and actions of the agent based on its perceptual inputs.
 

Types of Agents

1. Simple Reflex Agents

Simple reflex agents act solely based on the current percept and, percept history (record of past perceptions) is ignored by these agents. Agent function is defined by condition-action rules.

A condition-action rule maps a state (condition) to an action.

• If the condition is true, the associated action is performed.
• If the condition is false, no action is taken.

Simple reflex agents are effective in environments that are fully observable (where the current percept gives all needed information about the environment). In partially observable environments, simple reflex agents may encounter infinite loops because they do not consider the history of previous percepts. Infinite loops might be avoided if the agent can randomize its actions, introducing some variability in its behavior.

2. Model-Based Reflex Agents

Model-based reflex agents finds a rule whose condition matches the current situation or percept. It uses a model of the world to handle situations where the environment is only partially observable.

• The agent tracks its internal state, which is adjusted based on each new percept.
• The internal state depends on the percept history (the history of what the agent has perceived so far).

The agent stores the current state internally, maintaining a structure that represents the parts of the world that cannot be directly seen or perceived. The process of updating the agent’s state requires information about:

• How the world evolves independently from the agent?
• How the agent's actions affect the world?

3. Goal-Based Agents

Goal-based agents make decisions based on their current distance from the goal and every action the agent aims to reduce the distance from goal. They can choose from multiple possibilities, selecting the one that best leads to the goal state.

• Knowledge that supports the agent's decisions is represented explicitly, meaning it's clear and structured. It can also be modified, allowing for adaptability.
• The ability to modify the knowledge makes these agents more flexible in different environments or situations.

Goal-based agents typically require search and planning to determine the best course of action.

4. Utility-Based Agents

Utility-based agents are designed to make decisions that optimize their performance by evaluating the preferences (or utilities) for each possible state. These agents assess multiple alternatives and choose the one that maximizes their utility, which is a measure of how desirable or "happy" a state is for the agent.

• Achieving the goal is not always sufficient; for example, the agent might prefer a quicker, safer, or cheaper way to reach a destination.
• The utility function is essential for capturing this concept, mapping each state to a real number that reflects the agent’s happiness or satisfaction with that state.

Since the world is often uncertain, utility-based agents choose actions that maximize expected utility, ensuring they make the most favorable decision under uncertain conditions.

5. Learning Agent

A learning agent in AI is the type of agent that can learn from its past experiences or it has learning capabilities. It starts to act with basic knowledge and then is able to act and adapt automatically through learning. A learning agent has mainly four conceptual components, which are: 

1. Learning element: It is responsible for making improvements by learning from the environment.
2. Critic: The learning element takes feedback from critics which describes how well the agent is doing with respect to a fixed performance standard.
3. Performance element: It is responsible for selecting external action.
4. Problem Generator: This component is responsible for suggesting actions that will lead to new and informative experiences.

6. Multi-Agent Systems

Multi-Agent Systems (MAS) consists of multiple interacting agents working together to achieve a common goal. These agents can be autonomous or semi-autonomous, capable of perceiving their environment, making decisions, and taking action.

MAS can be classified into:

• Homogeneous MAS: Agents have the same capabilities, goals, and behaviors.
• Heterogeneous MAS: Agents have different capabilities, goals, and behaviors, leading to more complex but flexible systems.
• Cooperative MAS: Agents work together to achieve a common goal.
• Competitive MAS: Agents work against each other for their own goals.

MAS can be implemented using game theory, machine learning, and agent-based modeling.

7. Hierarchical Agents

Hierarchical Agents are organized into a hierarchy, with high-level agents overseeing the behavior of lower-level agents. The high-level agents provide goals and constraints, while the low-level agents carry out specific tasks. They are useful in complex environments with many tasks and sub-tasks.

This structure is beneficial in complex systems with many tasks and sub-tasks, such as robotics, manufacturing, and transportation. Hierarchical agents allow for efficient decision-making and resource allocation, improving system performance. In such systems, high-level agents set goals, and low-level agents execute tasks to achieve those goals.

Uses of Agents

Agents are used in a wide range of applications in artificial intelligence, including:

• Robotics: Agents can be used to control robots and automate tasks in manufacturing, transportation, and other industries.
• Smart homes and buildings: Agents can be used to control heating, lighting, and other systems in smart homes and buildings, optimizing energy use and improving comfort.
• Transportation systems: Agents can be used to manage traffic flow, optimize routes for autonomous vehicles, and improve logistics and supply chain management.
• Healthcare: Agents can be used to monitor patients, provide personalized treatment plans, and optimize healthcare resource allocation.
• Finance: Agents can be used for automated trading, fraud detection, and risk management in the financial industry.
• Games: Agents can be used to create intelligent opponents in games and simulations, providing a more challenging and realistic experience for players.

Overall, agents are a versatile and powerful tool in artificial intelligence that can help solve a wide range of problems in different fields.