If it is text generation, they use a text-generating model like ChatGPT. If it is image generation, they opt for Midjourney or Imagen. However, there’s a new development that’s changing how businesses use AI. And, that’s AI agents.
From 24/7 intelligent customer interaction to autonomous task execution at scale, AI agents have introduced new capabilities for businesses. Most of the capabilities these agents achieve were previously difficult or near impossible to achieve. Here’s a glimpse at the most in-demand agents and their impact.
Examples of AI Agents Businesses are Using Today
Plainly put, an AI agent is a software virtual assistant capable of perceiving its environment, making decisions, and taking action with little to no human intervention.
The environment is the world in which the agent operates. This could be in a room, a website, a game, or even the real world.
When in its environment, an agent uses sensors like cameras and microphones to sense or observe it. It acts or responds to certain situations in the environment as defined in its logic or algorithm. Examples include:
-
Web-interacting AI agents
These AI agents make sense of their environment (websites) and automatically execute actions like filling forms, navigating through pages, and clicking buttons. They are also capable of improving their strategies through trial and error or learning from new web structures and interactions.
Web-interacting AI agents combine multiple AI technologies, such as decision-making AI models and Natural Language Processing (NLP) chatbots. This enables them to execute repetitive web tasks and scale when necessary.
-
Resume screening agents
Rather than having the hiring team filter through resumes to find the most relevant candidates, resume screening agents handle this task. They are fitted with scoring AI models, parsing algorithms, and Natural Language Processing (NLP) to extract, format, and understand the information on resumes.
Most of these agents assess specific elements of resumes like keywords, education, relevant skills, and years of experience, to determine the best candidate. Some are even trained on past hiring decisions to understand what candidates make suitable hires.
-
Voice assistants
Voice assistants use dialog management systems to decide how to respond to certain requests.
While some may be programmed with pre-specified responses, some are designed to learn from a set of data and decide the appropriate response without human intervention. Behind the scenes, many businesses now use low-latency voice APIs and TTS engines to power these assistants, ensuring responses sound clear, natural, and immediate in real-world scenarios.
These agents power hands-free operations in business settings, enhancing productivity. You can also integrate them with other applications such as the calendar to serve as reminders.
-
Customer support chatbots
Customer support chatbots step in whenever human agents are not around. Some are configured to answer pre-defined questions based on customer preferences or past issues. Others are designed to learn from customer details and remember context across multiple conversations to provide the customer the correct information.
Apart from answering customer questions, some chatbots can complete tasks, such as processing a refund, escalating an issue, or resetting a password. Such bots are mostly integrated with backend systems like order management systems.
To understand why certain AI agents possess or lack specific levels of intelligence or capabilities, you must be aware of how AI agents are categorized. Here’s a comprehensive look into the different types of AI agents you should know. If you want to explore more about implementing such AI systems, an agentic AI course can provide hands-on experience with these technologies.
Understanding the Different Types of AI Agents
-
Learning agents
Learning agents are the most adaptive and advanced type of AI agents. This is because they can learn from their experiences or interactions with certain datasets or platforms and improve their performance and efficiency with time.
Rather than working from a fixed set of instructions, these agents are built to adjust their behavior based on the scenario at hand. To make this possible, they are fitted with a learning element that makes improvements as it learns from feedback.
They also have a performance element that makes decisions and carries out actions. After executing a task, there’s a critic element that evaluates how useful the outcome was. This way, the agent evolves continuously, making it possible for it to help out even when introduced to an unpredictable environment.
-
Utility-based agents
Unlike learning agents that do not follow fixed instructions to execute a task, utility-based agents use a model or formula to assess the effectiveness of a plan of action.
You give the agent a specific objective and it tries out different tactical approaches to reach the objective. For each trial, it assigns the course of action a utility score to determine the most effective approach.
These agents are mostly useful in environments where there are multiple approaches to achieve an objective or certain trade-offs exist. For instance, a stock trading bot uses a utility function to score each option before taking action. This is how the bot is capable of pursuing a smarter, more profitable strategy.
-
Goal-based agents
Goal-based agents care less about finding the most optimal course of action. They care more about which path is most likely to lead them to the goal.
So, when you assign them an objective, they assess the consequences of different choices before taking action. They choose the step of action with the highest likelihood of moving them closer to achieving the objective.
A good example of a goal-based agent is the GPS navigation system. When you select a destination, the GPS considers various routes while assessing the distance, traffic, and road conditions. Then, it selects the path that’s likely to get you to the destination efficiently.
-
Simple reflex agents
This is the most basic type of agent. It follows a set of predefined rules and works in an environment that’s well understood. It does not have any memory to record past events to predict future events, like a learning agent might do.
Simple reflex agents are effective in a fully predictable environment. The decision-making process is structured into a simple ‘if’ and ‘then’ approach. For example a microwave timer. Once the timer hits zero, it stops cooking. Also, if you open the door, it won’t start.
Final Words
Even though the most used AI agent is the learning type, you can stay ahead of the rest by combining the capabilities of different agents. Have one feed results to the other, making them work together to achieve a specific objective.
Remember, AI agents differ based on how intelligent they can get. Before you deploy an agent in a business setting, always consider the objective you want to achieve. Even though learning agents may shine in almost all objectives, it is wise to use the other agents whenever you have a task that suits them best.