What are AI Agents? Everything Businesses Need to Know

AI agents have been in the news recently as companies race to create these specialized tools for various roles and industries. But what does “AI agent” mean, and how might they impact businesses? In this article, we’ll cover the basics of this subset of artificial intelligence, the types of AI agents, examples of how they might be (and already are) used in real-world businesses, and where we anticipate the technology heading in the coming years.

What are AI Agents? Agentic AI Explained

AI agents, also known as agentic AI or LLM agents, are software entities that can understand their environment, process data, autonomously make decisions, and take action based on these decisions by interacting with their environment. Because of their complexity, AI agents are typically specialists in a certain area (e.g. fraud detection, healthcare, or teaching). This allows them to operate with minimal human intervention, adapting to changes in their environment with reasoning and executing decisions to meet predefined goals. Many in the tech industry believe that agentic AI is the next step in business efficiency, and recent announcements from Salesforce, Amazon, OpenAI, Google, Oracle, and more are pushing the industry in this direction.

Benefits of Using AI Agents

So why do businesses believe that AI agents are the next frontier in AI development? The benefits that agents could provide are immense:

Automation of Repetitive Tasks: Although generalized AI is already beginning to automate tedious activities, AI agents would take this to another level by not only automating more things, but performing them more effectively. This reduces human fatigue and frees up employee time to focus on more strategic work.
More Capable Decision-Making: Agents’ ability to perceive and react to changing environments improves their decision-making and reasoning by a significant margin. Not only can they adapt to evolving situations, but they can process real-time data faster than humans and make more informed decisions, more quickly.
Boosted Efficiency and Productivity: By either taking tasks off workers’ plates or helping streamline processes where possible, AI agents can bolster business productivity. Identifying bottlenecks and how to resolve them, providing actionable insights to analysts, or automating admin and documentation tasks all improve work productivity.
Enhanced Scalability: This improved productivity and efficiency also benefits the scalability of businesses, who can now leverage agents to help grow without exponentially increasing labor costs.
Better Adaptability and Learning: Agentic AI’s ability to learn and adapt over time enables it to continuously improve and refine processes. This keeps agents as effective as possible, even as businesses and industries change and evolve.
Reduced Costs: By reducing manual labor, optimizing workflows, and preventing costly errors, AI agents can significantly lower overall business costs. Decreased costs typically result in better financial stability, which facilitates more agility and aggressive innovation.
Improved Customer Experiences: Beyond the workplace, AI agents also have the potential to massively improve customer experiences via personalized recommendations, intelligent assistants, and automated support systems. Higher customer engagement and faster resolutions all lead to better customer satisfaction.

How do AI Agents Work?

Agentic AI’s adaptations rely on a complex process of perception, processing, action, and learning. Starting with perception, agents use sensors, APIs, or user inputs to gather real-time information about their environment to inform decision-making. This could include APIs for CRMs to check customer data or a user input specifying preferences for outputs. From there, agents use predefined rules, machine learning models, or reasoning engines to evaluate their options based on past and present data.

Based on this analysis, the agent may take action (or decide not to), influencing its environment in a way that aligns with its objectives. This might include re-categorizing rows in a spreadsheet based on new data, adjusting product pricing for an e-commerce site, or sending an alert to designated users. Some AI agents may incorporate feedback loops, improving and refining their analysis and decision-making through reinforcement learning or historical data. The result is an AI agent that is continuously honing its behavior to align with the business goals.

Learn more about types of AI/ML and how they’re trained here.

Agentic AI vs. Generative AI

The other major AI category in use right now is generative AI. This includes most of the popular models available today, such as ChatGPT, Perplexity, Gemini, and Claude. However, AI agents are different from generative AI in that they can actively interact with environments. While they may also be able to generate content (text, images, audio, etc.), their focus is on taking actions based on decisions they have made.

Types of AI Agents

Within the field of agentic AI, there are several sub-categories with different capabilities, limitations, and data processing methods.

Simple Reflex Agents

These basic agents operate fully on condition-action rules (if-then statements), not taking historical data, long-term consequences, or broader environmental context into account. As the name suggests, simple reflex agents are the most primitive form of an AI agent, simply reacting to specific inputs or stimuli with predefined responses or actions. Although basic, these agents are useful in situations where the correct response is always the same for a given input. Instances of simple reflex agents might include email spam filters or rule-based monitoring systems.

Model-Based Reflex Agents

Building on simple reflex agents, model-based reflex agents have an internal representation (or model) of the world. In other words, they’re able to use some knowledge of current or past states to inform their decisions. Instead of responding solely based on the input, an agent can reference this context to make more intelligent decisions. Examples could include customer support chatbots that remember past interactions, predictive maintenance models that track performance over time to predict failures, or cybersecurity threat detection that compares suspicious activity to historical data and patterns.

Goal-Based Agents

Taking decision-making a step further, goal-based agents not only account for environmental conditions, but also a desired outcome or goal. Instead of following a predetermined ruleset, these agents analyze multiple possible actions and choose the one that best achieves (or progresses towards) their predefined goal. Some real-world models of goal-based agents include systems that optimize the most efficient delivery route, BI tools that recommend action based on KPIs and company objectives, or virtual assistants that adjust reminders and notifications based on a user’s schedule and deadlines.

Utility-Based Agents

Instead of simply aiming for a goal, utility-based agents use a mathematical representation of preferences to weigh trade-offs and make decisions that maximize long-term benefit and overall utility. In other words, it not only tries to achieve a goal, but attempts to identify the best way to achieve that goal by considering factors like efficiency, cost, and risk. This structure comes much closer to how we think as humans, balancing instant gratification with delayed gratification. Potential instances of these agents include AI that dynamically adjusts product prices based on demand, competition, and customer behavior, or risk management systems that weigh various financial risks to recommend investment strategies or insurance policies.

Learning Agents

By using ML techniques like reinforcement learning, supervised learning, or neural networks, learning agents continuously improve their performance over time. This could include adapting to new data or refining their strategies to be more effective based on experience. Examples of learning agents include AI-powered recommendations on streaming platforms that refine suggestions based on user interactions or autonomous AI assistants that learn user preferences over time to provide more relevant help or advice.

Hierarchical Agents

The most complex of the agentic AI categories discussed here, hierarchical agents break their decision-making into multiple layers to manage complex, multi-step tasks more efficiently. They typically operate by starting with top-level decision-making to set overall objectives. From there, the agent will identify sub-goals and organize potential actions. Lastly, it executes the individual tasks to achieve its objective. A hypothetical instance of this might be a network of warehouse robots that coordinate to perform specific tasks. The overarching goal might be to categorize a pile of items into buckets based on color, so the AI would need to set sub-goals around criteria and manage each robot to perform the tasks.

Real-World Applications of Agentic AI: Hypothetical Examples

AI agents are already transforming industries at an unprecedented scale with cutting-edge automation, optimization, and decision-making. Let’s take a closer look at some hypothetical (but feasible) examples of AI agents being applied in the real world to improve efficiency and effectiveness.

AI-Powered Financial Advisory System: A financial services firm integrates AI agents into its wealth management platform to offer users real-time, personalized investment advice. These agents would learn a user’s investment knowledge, risk tolerance, and industry preferences over time to tailor their portfolio.

AI agent analyzes market trends, the user’s financial behavior, and their risk appetite
It then autonomously adjusts portfolio allocations based on predictive analytics
Personalized alerts and advice are sent to the user, such as a warning about a potential market downturn in industries relevant to their portfolio

AI Agents for Software Customer Support: A SaaS company adds AI-driven customer support agents to their website to independently handle technical troubleshooting. Over time, agents learn from common problems to recommend the most likely solution and can gather important context if a human specialist is required.

AI agent processes and understands the user’s query, offering instant solutions
If the issue requires more complex troubleshooting, it pulls from similar past experiences to test possible fixes
If the agent is unable to resolve the query, it can escalate the ticket to human support with details on all of the solutions that have already been tried

AI-Powered Healthcare Agents: A hospital deploys AI agents to optimize and personalize patient treatment plans in real time. The agents can react instantaneously to changes in body temperature, heart rate, responses to treatments, and more, improving patient care without stretching human doctors too thin.

AI agent processes a patient’s medical history and monitors their vitals (either in the hospital or via wearable technology like a watch)
Based on real-time patient data, it recommends personalized treatment plans and adjusts them as needed
If a potential complication arises, the AI agent can automatically notify medical teams before it escalates

Challenges & Limitations of Using AI Agents

Despite the immense potential of specialized AI agents that can operate on their own, some hurdles must be overcome:

Data Quality and Availability: AI agents need high-quality structured data to function optimally, and incomplete, outdated, or biased data can lead to poor decision-making. Because of this, it’s critical to implement robust data governance policies and real-time data validation.
Compliance Concerns: The lack of transparency from some models makes it difficult to justify or fully trust decisions, especially when it comes to situations like lending practices or healthcare decisions. Explainable AI (XAI) will be paramount for detecting bias and minimizing discrimination.
Integration Complexity: Legacy IT infrastructures may struggle to incorporate modern AI agents into their existing systems due to incompatibility. Gradual adoption of AI and use of hybrid AI-human workflows can help ease this transition in the interim.
Computational and Cost Constraints: As with any advanced AI model, agents require a lot of computing power and cloud infrastructure. The cost of training and running these AI might be prohibitive for smaller businesses, so look for scalable solutions as the technology comes down in price.
Security Vulnerabilities: Lastly, AI is so new that it’s difficult to predict how things like data poisoning (corrupting AI training data) will affect agents’ decision-making. This is why investing in AI-centric security mechanisms and training will be another key area for enterprises relying on these agents.

Business Impact

For businesses, AI agents present a huge frontier of innovation. As we’ve discussed, they can increase operational efficiency by automating repetitive tasks, making decisions faster, and minimizing human errors. They can also offer competitive differentiation opportunities from this AI-powered automation and efficiency, as well as enhanced predictive analytics and more comprehensive personalization for users. As AI reshapes job roles, these agents might result in human focus shifting towards more strategic and high-value tasks that are augmented by AI insights, driving business growth faster than ever.

When it comes to specific industries, retail might see AI agents dynamically adjusting product prices and tailoring shopping experiences. Finance will likely see advancements in fraud detection and algorithmic trading, while healthcare will enhance patient care via more accurate diagnoses and personalized treatment planning. Lastly, manufacturing companies can expect smarter automation and predictive maintenance to minimize downtime and reduce the costs of equipment failures.

Future Expectations for Agentic AI

With new AI models being released every week, it’s important to keep the future in mind as we evaluate AI and their impact on business. We anticipate specialized agents evolving into multi-agent systems where several models can collaborate for complex decision-making and task execution. They’ll also likely become more interoperable across platforms and industries, assisting a wider variety of businesses in more areas.

Governments will likely introduce more laws around AI transparency and compliance, such as explainable AI. These developments will open the door for AI to become more commonplace in regulated industries like healthcare, legal services, and civil engineering. Eventually, the natural progression of AI agents will be to move from simple task execution to higher-level strategic decision-making. This might involve businesses trusting AI to manage investments, hiring, and policy-making autonomously.

Invest in Software That Facilitates Your Business

Agentic AI likely represents the next frontier of intelligent automation for businesses. As it continues to evolve and innovate, it will become increasingly important for companies to strategically embrace new tools to enhance their operations and gain competitive advantages.

Now is the time for businesses to invest in their infrastructure and workflows, but it’s critical to make informed decisions. Finding the right fit is not as simple as looking for features that align with your project needs. Verified user reviews can provide valuable context around pricing models, customer support, long-term satisfaction, and more. TrustRadius collects thousands of reviews on cutting-edge and established tools for businesses so you can make the most informed choices possible. Companies can’t pay for placement or positive reviews, so you can rest assured that our data is unbiased and reliable. Browse some of the categories below to start today:

What Are AI Agents? April 2025 Guide