AI Agents: What Are They and What Impact Do They Have?
When you think of an AI assistant, your mind probably jumps straight to voice assistants like Siri and Alexa. However, these aren’t the only types of AI assistants out there. There are many different ways AI is helping us be more productive, not only in our daily lives but in professional settings, too.
AI agents are helping companies handle manual and repetitive tasks, like responding to thousands of customer inquiries without requiring any human intervention whatsoever. The result? Support teams that have more time to focus on those hard-to-crack customer cases.
In this post, we’ll take a closer look at what AI agents can do, how they work, their benefits, and some examples of how they’re operating in different industries.
What Are AI Agents?
An artificial intelligence (AI) agent is a type of software that can handle the tasks and responsibilities of human agents in fields like customer service. These AI agents can interact with users, make decisions, collect data, understand speech and text, answer questions, and solve problems without any human intervention. Since they’re based on different AI technologies, these agents also get smarter as they go, learning to improve responses and deliver more accurate results through different interactions.
What are the key components of AI agent technology?
Based on technologies like natural language processing (NLP), generative AI, machine learning (ML), and large language models (LLMs), AI agents can handle a variety of tasks like answering questions, solving customer issues, retrieving information, and a lot more. These technologies are what enable the architecture to function, creating an environment where an AI virtual agent uses percepts to affect the action it takes from a user input. Let’s take a closer look at the role of each of these technologies in AI agent architecture.
- Natural language processing allows AI agents to understand and process human language, whether that’s speech or text. With NLP, AI agents can decipher the intent behind a question for more natural interactions
- Generative AI powers the creative side of AI agents and is what enables them to create new content, like email drafts, content ideas, and responses to complex questions
- Machine learning is the backbone of AI agent architecture, allowing it to learn from data and improve over time, like learning customer preferences of predicting the next best course of action in a conversation
- Large language models give AI agents another layer of understanding, providing context and knowledge so that they can deliver detailed answers and complete complex tasks by instantly processing massive amounts of data
How do AI agents work?
There’s a lot that goes on behind the scenes of AI agent technology, most of which is invisible to the user. Below, we’ll take a brief look at how AI agents work and respond to users in a series of steps.
- Data collection: First, an AI agent is trained to gather data from different interactions along with customer transaction histories and other external factors. Once trained on a wide range of data, an AI agent can better understand queries in context.
- User input: Next, the user provides an input in the form of instructions or a command. The AI agent determines a goal from the input and may break down its actions into smaller tasks.
- Decision making: The AI agent will begin to analyze the input and organize the data. It can identify specific goals from the input and may break down its actions into smaller tasks or determine the best course of action based on previous experiences.
- Implementation: After a decision is taken, AI agents can begin to execute specific actions, such as answering a question or processing a request like tracking an order. These interactions are in real-time, meaning the user doesn’t experience any delays.
- Adaption: Finally, the AI agent will continue learning and improving with every interaction, using past experiences to refine their efficiency and provide even more accurate responses in future interactions.
Types of AI Agents
Not all AI agents are built equally. Some are designed to handle more complex requests, while others are very straightforward and follow a more strict set of rules. Below, we’ll look at the main types of AI agents and some examples of how they’re used.
Simple reflex agents
This type of AI agent uses a condition to define the action. Think of it as an “if this, then that” agent. They are built to react to current conditions without memory of the past, making them perfect for straightforward tasks like resetting passwords and responding to basic queries.
Model-based reflex agents
These agents take it up a notch by maintaining an internal model of their environment, meaning they can perceive things that aren’t immediately obvious. They don’t just react, they evaluate context and fill in gaps using stored data. For example, a smart irrigation system that adjusts watering schedules based on weather forecasts and soil moisture data relies on its internal model to anticipate future conditions and optimize water usage.
Goal-based agents
Goal-based agents are all about achieving objectives. They evaluate different paths and choose the one that best reaches their target. Think of a GPS navigation system that recommends the fastest route to your destination, adjusting for traffic and other obstacles.
Utility-based agents
These agents focus on optimizing outcomes by calculating “utility” or the value of different actions. For instance, an AI agent helping you book flights might weigh factors like price, layover time, and travel duration to recommend the best option.
Learning agents
Learning agents grow smarter over time by learning from past experiences. A good example is a recommendation system on a platform like Netflix that tracks your preferences and tailors its suggestions based on your viewing habits.
Hierarchical agents
These agents work in a team structure, with higher-level agents delegating tasks to lower-level ones. It’s like a manager breaking down a project into smaller tasks for team members, ensuring everything works toward a common goal.
Examples of AI Agents in Different Industries
AI agents are much more than just customer service bots that are built to answer questions. While these types of bots are highly efficient at relieving stress on customer support teams, there are many industries that are implementing AI agents for different reasons. Below, we’ll look at a few examples.
- E-commerce: AI agents can make it simple for customers to ask questions about inventory, order status, request refunds, and inquire about product information. When connected to internal systems, these agents can quickly pull information about stock and order management.
- Manufacturing: In the manufacturing industry, AI agents can predict maintenance needs, which can improve production and reduce downtime while keeping essential machinery running in top shape.
- Finance: In finance, AI agents can provide users with recommendations based on a customer’s specific financial objectives, easing the burden on human agents who can spend more time balancing high-value customer accounts
- Fleet management: For fleet management teams, AI agents can give managers an overview of fleet performance and maintenance needs with telematics, making it simpler to keep track of all the vehicles in a fleet and removing a lot of the manual need for human inspection
What Are the Benefits of Using AI Agents?
According to research by Gartner, 80% of companies are using AI to improve both agent productivity and the customer experience, demonstrating how essential this type of technology is today. There are many ways businesses stand to benefit from using AI agents, let’s look at a few key advantages.
Improved productivity and efficiency
AI agents operate independently without any human intervention, which means human agents are free to focus on more creative, strategic, or complex tasks. This can help teams better handle a high volume of requests and route more complicated tasks to the right department for quicker resolution.
Reduced costs
With AI agents working alongside existing teams, companies reduce costs related to handling incoming inquiries, onboarding new employees to handle increasing needs, and cutting down on opportunities for costly human errors.
Improved customer satisfaction
Customers feel more satisfied when they can get quick responses to their inquiries instead of waiting for hours or days for a response from your service team. Not only that, but AI agents are available 24/7, even when your service team isn’t, and can personalize customer experiences to improve engagement and loyalty.
Scalability
Unlike your workforce, AI agents can be easily scaled to meet evolving needs. Whether your company has an increase of orders to handle or there are more requests during busy season, AI agents can be easily scaled to handle multiple requests simultaneously and take on the extra load without straining your existing resources and budget.
Provides deep user insights
Since AI agents learn from each experience, these systems can collect valuable customer data by analyzing sentiment and interactions to get more information on behavior and preferences. Companies can use this insight to learn more about their client base, discover new trends, and make data-driven decisions.
What Are the Challenges and Risks Involved With AI Agents?
As with every new technology, there are some risks involved. However, with careful planning and a solid implementation strategy, these risks are no reason to avoid using AI agents, but they’re still important to be aware of.
- Data privacy: With any system that gathers and stores customer data, it’s critical to be aware of data privacy standards and best practices and to implement a privacy and security plan to stay compliant with regulations
- Ethical challenges: Sometimes, machine and deep learning models can lead to ethical issues and inaccurate or biased results, which is why it’s important to make sure you have quality data and human review systems in place
- Technical and computational complexity: Companies need to be well-equipped with existing IT infrastructure and computational resources to be able to handle AI virtual agent software and storage needs
- Multi-agent requirements: With multi-agent frameworks where specific tasks require multiple AI agents, there can be malfunctions and disconnections, which can make systems not only inaccurate and fallible but also vulnerable to cyber attacks
AI Agents: An Unlimited Future of Opportunities
The emergence of AI agents in business operations across different industries is changing both workflows and customer expectations for the better. With new technologies like aiOla’s speech AI leading the way, frontline workers can work smarter and safer while AI takes care of manual tasks. aiOla works alongside your team to help them complete inspections, collect critical data, trigger automations, and complete work processes all through the power of speech alone, making it an indispensable tool for companies who want to boost productivity without stretching resources.
Book a call with one of our experts today to see how aiOla is helping companies work smarter.
