Processing...
While artificial intelligence (AI) continues to dominate headlines, many business owners feel pressured to use AI agents to gain a competitive edge. However, the sheer volume of AI tools and capabilities that they might unlock can be quite intimidating. In this article, we’ll discuss the best AI agents across six major industries, discuss the current capabilities and limitations of AI assistants, and study how they operate. Additionally, we’ll examine the types of AI agents with examples, including model-based reflex agents, utility-based agents, goal-based agents, learning agents, and more.
AI agents are software tools developed with artificial intelligence to autonomously perform various tasks. Using advanced AI systems, these smart agents can solve problems and even make decisions with minimal human intervention. Backed by AI, machine learning, and deep learning, AI agents have the ability to reason, learn, and perform actions, aiding humans in automating complex tasks.
Artificial intelligence is already transforming numerous industries and enabling discoveries that push research and science forward. From customer support chatbots and recommendation engines to self-driving cars and Nobel-prize-winning protein discovery, AI is capable of tackling challenges that humans fail to undertake.
Let’s take a look at how AI agents are created and what mechanisms and technology make them possible.
The way AI agents work is by relying on advanced frontier AI models, also known as large language models (LLMs). After engineers build an agent, they integrate it into an environment and set operational parameters. This configuration allows AI agents to autonomously adjust their logic and perform actions as opposed to strictly following a set of predefined tasks.
The next step is allowing AI agents to reason and make decisions. However, for them to receive all the data required to act, agents are connected to third-party systems via APIs. In other words, AI agents rely on business systems to collect and analyze data to gain insight into the problem. After that, their training data becomes enriched, allowing them to apply the updated knowledge to self-correct and make data-driven decisions.
The continuation of the previous step, reflecting and learning is essential to enhance agents’ performance over time. By constantly self-evaluating and correcting, AI agents utilize feedback to learn and expand their knowledge base. This process is what makes agents autonomous, allowing them to troubleshoot obstacles, identify mistakes, and get better without any additional code.
Especially for larger projects, you can build a system of multiple agents to solve more complex and multifaceted problems. Multi-agent systems can comprise several agents specialized in different areas that can collectively resolve highly challenging tasks.
Before we dive into specific AI agents examples, let’s explore the common types: simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, learning agents, hierarchical agents, robotic agents, and virtual assistants.
The most basic type of AI agents, simple reflex agents perform tasks based on pre-programmed rules. Possessing limited intelligence and memory, simple reflex agents are designed to execute easy actions. For example, an AI-powered thermostat can turn the heating on and off, depending on the current temperature and certain thresholds set by a human.
By combining knowledge and real-time updates, model-based reflex agents can make timely and complex decisions. Model-based agents are perfect for unpredictable situations that require input from the environment. Self-driving cars exemplify model-based reflex agents as they continuously use sensors to avoid obstacles on the road and safely navigate the route.
Although goal-based agents also follow specific instructions to achieve their goals, they can also take in the environment to maximize their success. For instance, fitness apps can benefit from a goal-based agent by aligning workouts with users’ health objectives. Unlike simple reflex agents, goal-based agents are capable of evaluating data and planning actions per their goals.
Much more complex systems compared to their above-mentioned counterparts, utility-based agents take into account an array of parameters to make decisions. For example, utility-based agents can weigh potential risks and benefits of a certain treatment plan to find the best course of action for a patient. Utility-based agents map out all possible solutions, comparing various options to choose the optimal strategy.
Learning agents excel in changing environments where continuous learning is key. By collecting feedback, learning agents can leverage machine learning to deliver more accurate outputs. A primary example of learning agents is a spam folder that aggregates potentially fraudulent emails based on user feedback.
Consisting of an agent network, hierarchical agents deploy AI agents that collaboratively solve problems. Unlike rule-based agents, this manifold organizational structure allows for several layers of decision making. Hierarchical agents comprise systems that specialize in a particular niche, enabling them to solve complex tasks.
Robotic agents are the backbone of robotics, allowing complex hardware to perform specific tasks. Especially useful for dangerous or physically taxing tasks, a robotic AI agent senses its environment by utilizing a network of sensors and executes the appropriate action. Robotic agents are often used in warehouses and factories to minimize potential bodily harm when doing hazardous jobs.
Virtual assistants are designed using AI and natural language processing (NLP) to understand human language. These tools have been around for a while, but are becoming more advanced with machine learning techniques. Not only can virtual assistants like Siri process our speech and identify solutions, they can also learn from these interactions and offer a more personalized approach.
AI agents can fully automate repetitive tasks and speed up more complex projects by figuring out the best strategies. By minimizing human intervention, an agent program can increase operational efficiency and allow people to focus on the bigger picture. Additionally, their ability to process large amounts of data enables AI agents to perform tasks more precisely.
Moreover, when an agent evaluates their outputs, it can identify gaps in knowledge and self-correct, even without any input from humans. By learning through interactions and extracting data from other sources, AI agents can enhance the quality of their responses.
Finally, AI agents facilitate growth and expansion by adapting to the increasing workloads. While traditionally, business growth requires additional staff members, these rational agents can endlessly scale up without accumulating additional costs. Furthermore, they can reduce operational expenses by eliminating human error and removing inefficiencies.
Currently, even advanced AI agents have some significant limitations that have to be taken into account. For one, intelligent agents can get stuck in infinite feedback loops, leading to excessive resource consumption without any results. Although this can be avoided with proper AI agent design and planning, advanced agents still rely on substantial computational resources to perform.
In multi-agent systems, having even one faulty player can jeopardize the entire network. On top of that, if an agent becomes compromised, this poses a great concern to data privacy. Since AI agents often have access to sensitive data, organizations must invest in robust cybersecurity protocols and fault-tolerance mechanisms.
So, what are some examples of AI agents? In this part, we’ll go over the top industries that utilize smart agents and provide the best AI agents examples in real life for each category.
Among the top real-world examples for AI agents is the customer service industry that leverages AI capabilities that enhance customer experience. Let’s take a look at two use cases:
Among the most ubiquitous AI agents for business, chatbots can provide real-time support around the clock, majorly enhancing customer experience. Advanced intelligent agents can understand contexts, learn from historical data and past interactions, and execute intelligent decision making. Chatbots can also be integrated with internal business systems to gain insights into company and customer data to improve their problem solving.
Example: Zendesk provides AI solutions that deliver a complete customer service experience. One of the prominent generative AI agents examples, Zendesk’s tool can summarize customer queries, generate documentation and help desk articles, and answer requests. Using NLP, Zendesk customer service chatbots can break down the ticket’s intent and evaluate the sentiment, facilitating more thoughtful assistance.
Sentiment analysis allows companies to process customer data to evaluate how they feel about a certain product or service. By assessing customer reviews, responses, and other interactions, organizations can identify common issues and address them head-on. The sentiments can be categorized as happy, neutral, frustrated, etc., along with an explanation for the grade.
Example: Automaise is a platform that utilizes generative AI to streamline customer support. The tool combines several AI assistants to perform multiple functions, including decision automation, process optimization, and customer service.
AI agents are widely used in healthcare to improve patient experience, make hospitals more efficient, and minimize human error.
Modern intelligent agents can analyze highly complex medical images and identify patterns that could not be seen by humans. From X-rays and CT scans to MRIs, top AI agents can leverage their exceptional pattern recognition abilities to detect anomalies and early symptoms. Diagnosing patients early on can significantly enhance their chances for successful treatment, which is especially valuable with life-threatening diseases like cancer and heart problems.
Not only can AI agents identify new markers for illnesses, but they can also enhance the diagnostic precision and minimize false positives and negative outcomes. Additionally, healthcare organizations can deploy AI agents that focus on a personalized approach and identify the perfect treatment for a specific patient.
Example: Hippocratic AI is an agent tool that analyzes radiology images to detect lung cancer and other lung diseases. According to the company, their AI agents can detect cancer better than or on par with an experienced radiologist.
Among other notable examples of AI agents in healthcare is drug discovery. Arguably one of the most disruptive use cases, drug discovery helps create new treatments for various diseases. These intelligent agents can analyze enormous datasets describing molecular structures, protein breakdowns, clinical trials, and more, to predict the efficacy of a certain drug. Ultimately, AI agents have the ability to significantly accelerate drug development, cutting costs and delivering safer and more affordable treatment.
Example: HealthForce AI is a platform that parses millions of compounds to identify potential candidates for drug development. Aside from finding the best options, this AI agent can forecast how the drug might interact with the human body, effectively minimizing severe side effects.
In education, AI agents can personalize study programs, unburden teaching staff, and make learning more accessible to wider audiences.
AI agents can be used as virtual teaching assistants to streamline queries, especially in large colleges with hundreds of students. Having access to university information, these AI assistants can provide meaningful and timely answers to student requests without overwhelming human teachers. Using sentiment analysis, an agent evaluates the query, identifies the problem and emotion behind it, and diffuses the situation before it snowballs.
Example: Jill Watson is an AI agent developed by the Georgia Institute of Technology that provides learning assistance to the students. As a university that enrolls thousands of students each year, Georgia Tech struggled to answer countless student requests. As a result, students felt abandoned by their professors and began to drop out. The Jill Watson AI tool became the solution that replied to predictable questions, allowing professors to focus on complex tasks.
Another use case for AI agents is personalized learning that enables the creation of custom study programs for each student. Since everyone studies at their own pace, having access to a personalized curriculum helps to empower students to perform their best.
Example: Duolingo is one of the most successful uses of AI in mobile apps, offering courses in numerous languages. Duolingo’s AI agents develop personalized placement tests to determine the student’s level and create a tailored program to maximize the outcomes. The test adapts and self-corrects depending on the given answers, generating simpler or harder questions to match the user’s abilities. On top of that, Duolingo’s internal model relies on student performance to optimize their lessons.
The financial sector demands security and precision, both of which can be improved with AI agents.
Fraud detection is one of the most valuable use cases for AI agents for business. By analyzing transactional data in real time, these AI agents can determine common markers indicative of fraudulent behavior. In addition to stopping fraud and money laundering, these systems allow financial companies to handle more transactions without worrying about security.
Example: Mastercard is a behemoth of finance, dealing with over 125 billion transactions every year. Despite the current limitations of GenAI, Mastercard managed to leverage this technology to extrapolate complete card details from partially visible credentials. This helps the company immediately flag compromised cards whenever they enter their internal model and alert banks.
Learning agents are also used in trading to help customers manage their investment portfolios based on the existing market situation. In today’s volatile market, having access to real-time updates is key to making quick and intelligent decisions. By analyzing historical data, AI agents can derive patterns and trends and make predictions about future consequences of market fluctuations. Different agents working together can perform trades, analyze risks, and optimize user investment portfolios to maximize their earnings.
Example: Betterment is among the real-world examples of using AI in trading. Their goal-based agents help users achieve their financial objectives, while their higher-level agents assist users in adjusting portfolios to meet the ever-changing market conditions.
An ever-growing sector, e-commerce utilizes the top AI agents to provide personalized recommendations, build dynamic pricing systems, and manage inventory.
Intelligent agents are also widely used to deliver personalized recommendations to improve customer experience and boost sales. Not only can these tools gather user data to generate marketing campaigns that speak to particular audiences, they can also offer tailored products for each customer. Based on user purchase and browsing history, interests, occupations, and other demographic data, AI agents can accurately predict what the user might need next.
Example: Braze is a customer engagement tool that relies on AI agents to provide personalization in shopping and communication. Their advanced learning agents help businesses offer tailored experiences to their customers, including personalized suggestions, dynamic pricing systems, and more.
Among other AI agents examples is inventory management, an essential yet complicated part of the retail industry. Intelligent agents can keep track of inventory levels, predict future demand, and automate restocking. Higher-level agents can even prioritize high-margin items during shortages, maximizing the retailer’s profits.
Example: Prediko is an AI inventory management agent that offers accurate forecasts of demand, monitors stock health, and makes timely purchases. Aside from its predictive capabilities, Prediko delivers a high grade of customization, allowing retailers to tailor this multi-agent system to address their specific needs and circumstances.
Finally, manufacturers can also benefit from AI agents through unlocking predictive maintenance and optimizing supply chains.
In supply chain management and manufacturing, gaining predictive abilities is a game changer. Collecting and analyzing real-time data enables manufacturers to continuously monitor the health state of their infrastructure and detect potential issues early on. Access to these insights helps organizations reduce downtime, prolong the lifespan of their critical equipment, and minimize costs.
Example: Akira AI is an agentic AI system designed to optimize infrastructure management and deliver reliable maintenance. While lower-level agents gather environmental data like temperature and humidity, other agents analyze the accumulated findings to detect patterns in system performance. This allows companies to discover early signs of deterioration and address them before machines break.
Among other priceless AI agents business impact examples is supply chain optimization. A highly complex area, the supply chain is prone to disruptions due to market conditions, geopolitical situations, inventory levels, and more. To combat that and minimize bottlenecks, companies rely on AI agents to analyze various scenarios and discover efficient production schedules, routes, and suppliers.
Example: Leeway Hertz utilizes generative AI models to optimize supply chain management and logistics. Using AI agents, the company helps businesses identify the optimal delivery routes, forecast demands, and detect anomalies. As a result, their customers benefit from reduced fuel costs, shorter delivery times, and high-quality suppliers.
If you’re interested in leveraging cutting-edge technology to take your business to the next level, consider developing a custom AI agent for your needs and objectives. NIX is a software development company with decades of experience that keeps up with the latest technological breakthroughs to empower its clients. Get in touch with the NIX team to discuss your project and build a comprehensive system of AI agents that help you achieve your goals.
01/
AI agents can execute repetitive tasks that take too much time for human specialists. Meanwhile, the staff can concentrate on critical actions and complex tasks. For example, AI agents can automate customer support, data entry, routing, and more.
02/
AI agents aren’t here to replace humans but rather to take on mundane tasks while allowing them to focus on the bigger picture. Additionally, they can identify trends and patterns that aren’t visible to the human eye, empowering employees to make smarter decisions.
03/
There are some limitations that even the best AI agents currently have, such as excessive computation resources, cybersecurity issues, and agent dependency. For instance, if one agent in a multi-agent system fails, this can affect the entire workflow, posing a threat to the company and customer privacy.
04/
From eliminating human error and delivering predictive capabilities to enabling self-driving cars and endless scalability, AI agents offer an array of key benefits for businesses across industries.
Be the first to get blog updates and NIX news!
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
SHARE THIS ARTICLE:
Schedule Meeting
