What are AI agents, and how do you implement them?

The global autonomous AI and autonomous agent market is growing fast. It was worth $4.8 billion in 2023 and is expected to skyrocket to $28.5 billion by 2028, growing at a CAGR of 43%.

So, what are AI agents and how do they operate?

An AI agent is a sophisticated software program designed to interact with its environment and perform specific tasks by mimicking human intelligence. It can analyze data, make decisions, take actions autonomously, learn from past experiences, and adjust its behavior over time.

AI agents can be physical, like a smart thermostat, or software-based, such as a virtual personal assistant living inside an enterprise resource planning (ERP) system.

This technology became popular after the emergence of OpenAI’s ChatGPT, as this large language model makes it easier for computer programs to understand human language and interact with people. Generative AI can also handle large amounts of data and analyze it faster than classic AI. But it’s worth noting here that not every AI agent has to be based on Gen AI. It can function just fine with pure artificial intelligence, depending on its field and responsibilities.

Check out our recent article on Gen AI vs. artificial intelligence to understand the difference between these technologies.

What differentiates AI agents from AI-powered chatbots and robotic process automation software is that AI agents are rational. They make decisions based on the data they receive and their interaction with the environment. They can react differently to the same situation, depending on the input and the environment. Users don’t have to constantly send prompts, like in ChatGPT, to force an AI agent to act. It will make decisions and take intermediate steps independently to reach the final goal.

Chatbots are built to interact with humans, while AI agents are supposed to work autonomously and only contact end users to deliver the final response or escalate a task if they don’t have enough information to see it to completion.

You can distinguish AI agents from other software through the following characteristics:

AI agents come in different types. Below, you can find each type explained with an example.

Their response to stimuli is based on predefined rules without considering possible consequences or past events. If their environment changes, simple-reflex agents can’t adapt unless someone changes the rules that guide their behavior. You can say these AI agents have “limited intelligence,” but they are effective in static environments, executing tasks that depend solely on the current perception.

One example of a simple-reflex AI agent is a basic smart thermostat without self-learning capabilities. It operates autonomously, using a temperature sensor to stabilize room temperature. When the temperature falls below the threshold, the AI agent activates the heater. When it exceeds the set point, the agent turns the heater off. Its behavior is purely reactive, relying on if-then rules.

They gather information about the world and maintain a model that helps them infer data that is not explicitly presented. Unlike the previous AI agent type, model-based reflex agents respond to stimuli by considering experiences and probable outcomes. These AI agents can also function in dynamic environments, as they can update their intrinsic model according to changes in the world.

You can take an intelligent vacuum cleaner as an example of a model-based reflex AI agent. It navigates and cleans a house by using an internal model of its environment. This agent continuously receives input from sensors that detect obstacles, dirt, and floor types. Based on this data and its internal map, it determines the most efficient cleaning strategy, like adjusting suction power on different surfaces.

These AI agents possess superior reasoning skills compared to the previous two types. They can investigate several paths leading to the end goal and choose the most efficient one. Goal-based AI agents can generate a list of sub-goals that they will follow. They will take action only if it moves them closer to the final goal.

An AI chess player exemplifies a goal-based AI agent by striving to win the game through strategic planning. It uses a comprehensive model of the chessboard and follows the rules of the game to evaluate possible moves. This AI agent sets a primary goal of achieving a checkmate against the opponent while minimizing the risk to its own pieces. It tries to anticipate the opponent’s moves and assess the outcomes of various strategies.

Utility-based AI agents evaluate different approaches and select the best-suited path toward the end goal based on utility (preference). If you use a utility-based agent to plan your trip to some destination, you can specify your preference to get there as soon as possible or as cheaply as possible, and your AI agent will select the corresponding course of action.

Let’s assume that a smart thermostat operates in a utility-based mode where the preference is energy efficiency. This thermostat uses sensors to monitor the current temperature and humidity levels, but it will also consider energy costs. It will lower the temperature slightly during peak energy prices, even if the house is fully occupied.

These AI agents can learn from their surroundings and experience. They begin with a limited knowledge set and expand their horizons with every completed task. These agents are a perfect fit for dynamic environments, as they can adapt automatically without the need for manual rule reprogramming.

A personalized recommendation agent is an example of a learning AI agent, as it continuously improves its suggestions by learning from user behavior and preferences. Initially, it recommends content based on its general popularity. As the user interacts with the platform, watching shows and movies, rating content, and browsing different genres, the AI agent collects and analyzes this data to understand the user’s preferences and make personalized content suggestions.

These AI agents are organized in a hierarchy where higher-level agents decompose a task into subtasks and distribute them to lower-level agents. Lower-level AI agents execute these subtasks and report the results to their superiors.

A self-driving car can be seen as an example of hierarchical AI agents. A high-level planning agent determines the optimal route to the destination, considering traffic conditions and legal restrictions. It delegates tasks to mid-level agents responsible for specific behaviors like highway driving, city navigation, and parking. These, in turn, instruct low-level control agents to perform precise maneuvers such as steering, accelerating, and braking.

A typical AI agent has six components:

After learning what AI agents are, let’s see how they function. For the sake of simplicity, we can break down AI agents’ workflow into five high-level steps.

Recent research by Accenture shows that 96% of executives are confident that AI agent ecosystems hold promises for their organizations in the coming three years. So, which areas can they benefit the most? Let’s explore.

There are many benefits for AI in healthcare. What do AI agents mean for the field?

When it comes to patient treatment, AI agents can analyze patients’ symptoms and medical history, direct them for a scan if needed, analyze the resulting medical images, and report to the assigned doctor with a suggested diagnosis and a personalized treatment plan.

For instance, Healthcare Advisor AI from Tars can interact with patients, assess their symptoms, offer personalized recommendations, send educational materials and appointment reminders, and more while helping patients navigate their health conditions.

AI agents can also optimize hospital workflows by predicting patient admissions and improving resource allocation. For instance, an AI agent can predict that ten patients will be admitted this evening, and it will immediately adjust doctors’ schedules to accommodate this assumption.

These AI agents can tend to customers 24/7, giving efficient and personalized support. They can answer questions, process refunds, troubleshoot technical issues, and much more. So, an AI agent receives a request, accesses the company’s database, the Internet, or any other source, processes this information, and takes action or escalates the matter to a human operator. AI agents learn from each interaction and can provide better responses over time.

The generative AI agent startup Beam offers ready-made, pre-trained AI agents for customer service, along with an opportunity for companies to customize their own tools. If you opt for off-the-shelf AI agents, you can deploy them in seconds. They are GDPR compliant and can run locally or in the cloud.

AI agents can take over predictive maintenance, product quality assurance, and factory floor monitoring.

Unlike regular artificial intelligence models that can also detect anomalies, AI agents can react. They have actuators, which allow them to adjust device parameters, activate an alarm system, stop a conveyor belt, and more.

AI agents acting as financial advisors can communicate with customers, analyze market trends, consider customer preferences, and suggest assets for customized investment portfolios. AI agents can also autonomously manage these portfolios and protect them against market fluctuations.

Another exciting example of AI agents in finance is fraud detection. These agents can study cybercrime patterns and monitor user activities and financial transactions in real time to detect fraudulent behavior. They can flag suspicious transactions and terminate them automatically. AI agents can also learn to scan financial software for security vulnerabilities and fix or report them before malicious actors take advantage.

If you are interested in this sector, you can learn more about Gen AI in finance and banking on our blog.

From autonomous vehicles to logistics and delivery route optimization, AI agents are thriving in the transportation domain. Self-driving cars rely on AI agents to navigate, avoid collisions, abide by traffic rules, and continuously adapt to their environment. In addition to driving, AI agents can monitor the vehicle for anomalies and alert its manager.

When it comes to logistics and delivery, AI agents can analyze road conditions, weather, and vehicle performance and combine this with delivery schedules for optimal route planning. They can process real-time traffic data, autonomously redirect vehicles to less congested routes on the fly, and adjust delivery schedules accordingly. If you need more inspiration, check out our blog post on AI use cases in supply chain.

If you live in Phoenix or San Francisco, you can already try the autonomous ride-hailing service from Waymo. You won’t see a human driver in the cab. These cars are fully operated by AI agents. They rely on a comprehensive set of sensors to navigate during the day and at night. Each autonomous car is trained on over 20 billion miles of simulation and has around 20 million miles of real-road driving experience. Waymo claims their cars minimize accidents and injuries in the areas where they operate.

So, can companies already build and successfully adopt AI agents in their current state? Or is the technology still in its infancy and can only offer limited benefits? ITRex CTO, Kirill Stashevsky, explains.

Theoretically, it’s possible to implement any of the AI agents described in this article. However, in practice, it will be a challenge to achieve accurate and predictable results at scale. For instance, it takes a few weeks to build a workable prototype of a teaching assistant AI agent based on a GPT interface that will produce results with 60% accuracy, but it will take at least ten months to take the accuracy level up to 90%.

And don’t expect a smooth, gradual improvement. AI models have a black box architecture, meaning it’s often hard to tell what exactly is wrong and which algorithm is skewing the results. You can raise an agent’s accuracy level to 70%, and the next day it drops to 50%, and you are left guessing why.

Generally speaking, AI agents in their current state are best suited for applications where slight output variations are acceptable. Otherwise, we strongly recommend implementing human verification mechanisms.

Today, AI agents have two key limitations:

Other limitations include:

AI agents can serve as a powerful tool to automate tedious tasks and streamline operations. However, in their current state, be conscious when entrusting them to perform critical tasks because, ironically, while being used to minimize human error, AI agents themselves can make mistakes. Even AI models built by giants like Google. Just recently, Google’s large language model Gemini was generating historically impossible images, such as Nazi soldiers with Asian features.

At ITRex, we can help you build AI agents based on publicly available platforms or develop them from scratch. We understand which obstacles to expect during implementation and how to move forward. Our diverse team has experience in machine learning, large language models, and generative AI development, as well as in IoT and embedded systems. We have dedicated data consultants who can help you with data strategy and management.

Our developers can build an AI agent pilot version that operates in a controlled environment in just a few months. But achieving stable and predictable results with such a complicated system takes around 10 months of testing and experimentation, depending on your desired level of accuracy and stability.

We also have an AI PoC service that can help you speed-test and validate your AI concept.

If you want to start by implementing regular AI, check out our guide on how to create an AI system and a post on how to implement AI in your organization (with a link to a free eBook inside).