Rational AI Agent: Models, Examples, Types

In today’s competitive market, businesses are always looking for ways to do more without sacrificing quality or hiring additional staff. The growing capabilities of AI are helping businesses like yours accomplish this through the use of rational AI agents. These virtual coworkers help streamline workflows across industries to boost productivity, and can even process information to generate new insights that lead to innovation and growth.

What is a rational AI agent?

A rational AI agent is an autonomous entity or system that uses artificial intelligence, game theory, and logical reasoning to make rational decisions, similar to how people think and make decisions in the real world. Rational AI agents process information and then select the best approach to achieve specific goals.

Not only are these agents capable of making decisions or solving problems independently, but they can also act on your behalf, offering you insights into how people typically think about problems and make decisions.

You can use rational AI agents in many fields and applications, including:

• Virtual assistants: These agents automate tasks and help you make business decisions.
• Stock trading: AI agents can analyze market trends and make buying or selling decisions.
• Healthcare: Rational agents partner with providers to diagnose patients or develop personalized treatments.
• Industrial robotics: Autonomous agents use sensors to understand their environment and perform various tasks.

Types of rational AI agents

All rational AI agents are designed to perceive their environment and use logic and reasoning to make decisions. However, the level of intelligence and capabilities an agent has can be categorized into five distinct types:

• Simple reflex agents: This action-based agent is the most basic of all types. It uses a simple rule set to map sequences and make decisions based on reactions to immediate environmental stimuli but lacks an understanding of the actual situation. Simple reflex agents can be used to control drones or operate autonomous vehicles, for example. Since this model is less complex, it can be easier for people to use; however, these agents don’t offer a learning component to improve performance based on experience.
• Model-based reflex agents: While similar to simple reflex agents, model-based reflex agents go a step further by incorporating models to help make decisions. This rational AI agent compares current data with models to develop contextual awareness, make decisions, and predict future outcomes. Instead of just reacting to the environment, this agent can act more intelligently and even interact with people in meaningful ways. Model-based agents power many AI-powered apps and virtual assistants.
• Goal-based agents: In terms of capabilities and intelligence, goal-based agents fall in the middle. These agents work autonomously toward preset goals and use logic to determine the most efficient and optimal outcome. Goal-based agents can handle anything from simple workplace automation to more complex challenges, such as perceiving and avoiding obstacles while navigating routes. Additionally, this type of agent can adapt and learn from its environment, empowering it to handle changing situations.
• Utility-based agents: These agents are more sophisticated than the previous three, programmed with complex reasoning algorithms designed to maximize their utility in decision-making. Rather than simply pursuing a specific goal, utility-based AI agents assess all the potential actions and weigh the potential outcomes against the costs to select the most beneficial option for reaching the goal. This type of agent works well in highly complex or rapidly changing environments, such as finance, because it can balance risk when weighing investment decisions.
• Learning-based agents: This type of agent can learn from its experience and even change how it makes decisions, improving over time. Learning-based agents accomplish this by using sensory inputs and feedback mechanisms, allowing them to adapt their behaviors and decisions based on their previous outcomes and changing environments. The agent learns how to optimize its decision-making process so there’s an increased likelihood of meeting its goals.

Components of a rational AI agent

Rational agents in AI use specific components to determine the best possible outcome based on the goals, environment, data, and more. Their key components include:

Perception and sensing

Rational AI agents rely on physical and virtual sensors to perceive their surroundings. The sensors may include cameras, temperature sensors, microphones, and other data inputs for the agent to understand its environment, gather information, and make data-driven decisions.

Knowledge representation

Also called the knowledge base, rational AI agents access this centralized collection of data and resources on specific topics related to their functions or goals. For example, a healthcare-based agent’s knowledge base would include diagnostic criteria. It may also contain information about the environment or the agent itself, such as previous outcomes. The structure of the knowledge base is designed to help the agent make informed decisions.

Internal state

This component includes the agent’s current understanding of its environment based on its past actions and perceptions. It enables the agent to draw on past experiences and consider future possibilities when making more complex decisions. An internal state is key for rational AI agents working in dynamic or fast-paced environments where they need to monitor changes and adapt their decision-making accordingly.

Learning component

Agents with learning components can refine their models and improve performance by learning from past outcomes. Ranging from simple feedback loops to more advanced machine learning (ML) algorithms, this component allows agents to adjust strategies and improve the odds of achieving their goals.

It’s important to note some types of rational AI agents don’t have dedicated learning components, including simple reflex agents, model-based agents, and goal-based agents. However, model-based and goal-based agents can still adapt and learn from previous outcomes.

Decision-making and planning

Rational AI agents make decisions and plan actions based on rules or models. These can be as simple as “if, then” rules or use more complex ML algorithms and other models that offer a higher level of reasoning to make rational decisions. They also rely on pre-defined performance metrics to evaluate progress toward their goals and optimize their rational decision-making.

Actions

Finally, these agents are capable of taking action. Software-based rational AI agents can initiate various digital actions, such as placing orders, sending emails, or adjusting performance metrics within analytics systems. Physical agents, such as robots, may use actuators like wheels, arms, and motors to perform actions in real-world environments.

How a rational AI agent works

Rational AI agents perceive their environment, make decisions, and take actions autonomously, all in an effort to achieve predetermined goals.

Here’s the step-by-step process of how these agents determine the best possible outcomes:

1. Observe the environment: A rational AI agent first has to perceive its environment using physical sensors and data inputs. For an agent in an autonomous car, this step may mean using cameras to detect weather conditions and surrounding traffic volumes. On the other hand, a rational agent focused on customer support tasks may observe and analyze website and customer data.
2. Understand its goal: Each agent has its specific goals it aims to achieve, which are predetermined and defined by the agent’s performance metrics. In this step, the agent understands what it has to accomplish to successfully meet its goals. For example, a marketing-based virtual assistant may have a goal of increasing social media revenue more efficiently.
3. Make decisions: The agent will use its built-in knowledge, your knowledge base, and other inputs to assess the outcomes of different decisions. Depending on the type of agent in use, it may also evaluate and learn from past outcomes to aid this process. The agent ultimately makes a decision based on which will enable it to achieve its goal most effectively. The decision-making process is more straightforward for simpler agents and “if, then” scenarios, but it can become more complex for dynamic or complicated scenarios, such as stock trading or operating an autonomous vehicle.
4. Incorporate feedback: Many rational AI agents are capable of learning from past outcomes, such as whether they met their goals or not. For instance, if an agent understands that a particular decision or action got it closer to meeting its goal, it is more likely to repeat that action in the future. On the other hand, if an outcome was unfavorable, the agent will retain this information and is much less likely to pursue that course of action when faced with similar scenarios in the future. This learning process is essential for helping a rational AI agent perform better over time.
5. Take action: At this point, the agent has come to a decision and takes a course of action that it believes will meet its goals or achieve its performance metrics. This step involves the agent interacting with its environment, either through physical actuators like wheels or robotic arms, or through digital outputs, such as a customer service agent holding a conversation with a customer.
6. Continuously repeat the process: Rational AI agents repeat this entire process, from perceiving to making decisions and taking action, over and over again to learn and improve their performance.

Intelligent agents vs rational agents

While intelligent agents and rational agents both perceive their environment and make decisions to reach goals, they achieve this through different operations. Below, we’re sharing the definition of each agent and key differences between the two to better understand their unique characteristics and capabilities.

Examples and use cases of rational AI agents

You can use rational AI agents in a range of diverse business applications. Below are a few examples to consider.

Investing and trading

Financial and investment firms use rational AI agents to assist with, or even automate, the trading process. These agents analyze current and past market data to identify trends and make buying or selling decisions that meet an investor’s goals while maximizing returns. In addition to stock trading, rational AI agents can help mitigate risks and detect fraud in financial institutions.

Healthcare

Rational AI agents are great tools in assisting clinicians with diagnosing conditions and developing treatment plans. An agent can analyze medical research and patient data to evaluate different treatment options before recommending the best course of treatment for an individual patient. It even tailors its recommendations based on a patient’s preferences and treatment goals. Other types of AI agents can assist medical offices with administrative tasks, such as scheduling appointments or streamlining the coding or billing processes.

Automation and robotics

Self-driving cars, autonomous drones, and robot vacuums represent a wide range of devices that use rational AI agents with varying complexity to make decisions. For instance, robot vacuum cleaners use AI agents with a simple decision-making process and goal: getting floors clean. On the other hand, autonomous drones and vehicles rely on more complex rational agents to learn and make decisions in changing environments.

Customer support

Rational AI agents can interact with your customers or clients through chatbots and even have human-like conversations with them thanks to natural language processing (NLP) technology. These agents handle support tasks like tracking online orders and answering frequently asked questions. They can also tailor product or service recommendations based on data and customer preferences to help you make more sales.

Dynamic pricing

Companies like Uber and Amazon use dynamic pricing to automatically adjust pricing based on availability and demand. Rational AI agents empower dynamic pricing by analyzing real-time data—including inventory, customer behavior, and competitor pricing—and making pricing decisions that meet their goals of optimizing sales and profit.

Analytics and business intelligence

AI agents also help people automate analytics and BI tasks, including collecting, processing, and cleaning of data for analytics use. Rational AI agents speed up this process and increase the accuracy of your data by eliminating manual entry and human errors from the process, reducing problems caused by incomplete, inaccurate, or duplicate data. An agent can also make your data more accessible through NLP, so people can find answers through simple question-based queries instead of needing to know programming language.

You can experience the power of rational AI agents with Domo. Our AI agents automate repetitive tasks and improve productivity so you can develop more successful strategies and grow faster. See how our AI agent and service layer can streamline your analytics practice and enrich your data for better outcomes.