What is an intelligent agent? Definition, use cases and benefits

What are agents in artificial intelligence?

Agents that use artificial intelligence (AI) and machine learning can gather data through sensors, such as microphones and cameras, to perceive the real-world environment. Their decisions can drive actuators, such as speakers and displays, to deliver output to the user. Output can be in the form of text-to-speech where users hear audible output. The practice of having an agent bring information to a user is called push notification technology.

A common characteristic of intelligent agents is adaptation through learning and optimization. This is based on experience, real-time problem-solving, analysis of error or success rates, and the use of memory-based storage and retrieval.

Enterprises often use intelligent agents in data science applications, such as data mining and data analytics, as well as for customer service and support. Intelligent agents are a vital part of AI-driven business workflow automation. Consumers use them to compare the prices of similar products, receive purchase recommendations and get notifications when relevant events occur, such as a website update.

Intelligent agents are similar to software agents, which are autonomous computer programs.

Why intelligent agents matter for enterprises

Intelligent agents have become vital for the enterprise, providing a range of benefits. Cloudera's The Future of Enterprise AI Agents 2025 report found that 96% of the 1,500 enterprise IT leaders it surveyed plan to expand the use of AI agents in the next 12 months. Specifically, they are aiming to develop performance optimization bots (66%), security monitoring capabilities (63%) and development assistants (62%).

Intelligent agents can help in the following areas:

• Automate complex tasks. Business workflows are cumbersome and complex. Intelligent agents can automate many of these tasks. They provide faster outcomes with fewer errors, while maintaining data security, meeting compliance demands and establishing consistency that's difficult to maintain with human processes.
• Improve efficiency. Intelligent agents can operate around the clock, ensuring service anytime it's needed. By tackling mundane tasks, it also frees human employees to operate at a more strategic level and focus on new opportunities that add business value.
• Drive business decision-making. Traditional businesses rarely are able to make decisions based on all available data. Intelligent agents collect and analyze enormous amounts of data to make predictions, suggest new opportunities and guide business decisions in real-time. They can also respond quickly in dynamic and fast-moving business conditions.
• Enhance user experience. Intelligent agents are effective analytical platforms able to assess and predict with startling accuracy. They're ideal for customer-facing tasks, such as product recommendations, personalization engines and personalized assistants. These capabilities improve user experience, drive revenue and build brand loyalty.
• Connect siloed data sources. Businesses possess vast volumes of seemingly unrelated data that's often overlooked or ignored in everyday decision-making. By accessing and processing all this data, intelligent agents eliminate data silos and identify information and trends that human analysts might overlook.

How intelligent agents work

Intelligent AI agents use a recursive process of perception, planning, action and learning in the following ways:

• Perception. This is the process of gathering information obtained from many sources, including user input, knowledge bases, internet content, business data sources, and internet-of-things, and real-world devices, such as cameras or microphones. Perception also can include preprocessing of input data, such as breaking down a user prompt into tokens or analyzing a camera image to identify an object
• Planning. Also called reasoning, planning is the use of algorithms and analytics to process collected data. The agent considers the user's intended goal and then plans a course of action to achieve it. This can involve finding answers to a user's question or determining a series of steps affecting devices in the real world, such as steering an autonomous vehicle. To do this, the agent often will break a process into numerous subtasks.
• Action. This forms the execution phase of the agent's behavior where the planned steps are carried out. Actions include interfacing with data sources, collaborating with other agents, using application performance interfaces and interacting with systems or devices to complete each step. Errors or unforeseen results from a step must be clarified or reinforced by a human. The action phase is finished when the agent renders a decision, yields requested data or completes a real-world action.
• Learning. The agent determines the difference between the intended or requested outcome and the actual outcome. Ideally, the intended and actual outcomes are identical, and the agent yields an optimal result. However, there can be unexpected or suboptimal outcomes. By measuring the difference and understanding the underlying reasons, the agent learns and optimizes its performance for future iterations. In some cases, the difference is measured objectively. In other cases, human operators provide feedback on the results.

Types of intelligent agents

Intelligent agents are defined by their functions, capabilities and intelligence as follows:

• Simple reflex agents. These agents function in a current state, ignoring past history. Responses are based on the event-condition-action or ECA rule, where a user initiates an event and the agent refers to a list of preset rules and preprogrammed outcomes. Simple reflex agents are often the basis of straightforward chatbots.
• Model-based reflex agents. These agents work in the same way as reflex agents, but they have a more comprehensive view of their environments. A model of the world is programmed into the internal system that incorporates the agent's history. The agents typically have access to more comprehensive data and previous experience. They're used in more sophisticated AI environments, such as autonomous vehicles.
• Goal-based agents. These agents, also referred to as rational agents, expand on the information that model-based agents store by including goal information or information about desirable situations. This lets them formulate the steps or courses of action needed to accomplish a goal. They're frequently used in analytical AI, such as chess AI, business automation and optimization systems.
• Utility-based agents. These agents are similar to goal-based ones, but they provide an extra utility measurement that rates each possible scenario on its desired result and then chooses the action that maximizes the outcome. Rating criteria includes the probability of success and the resources required. These agents are typically used in vertical AI platforms, such as financial trading or healthcare diagnostics.
• Learning agents. These agents can gradually improve and become more knowledgeable about an environment over time through a learning algorithm. The learning element utilizes feedback on performance measures to determine how performance elements should be adjusted to gradually improve the agent's performance. This concept describes how an AI system should operate. Most intelligent agents have some learning capabilities, but learning agents are tailored to optimize future behaviors to achieve better and more efficient outcomes.

Examples of intelligent agents

AI assistants such as Alexa and Siri are examples of intelligent AI agents that use sensors to perceive a user request and automatically collect data from the internet without the user's help. They can gather information about their observable environments, such as weather and time.

Google Assistant is another example of an intelligent AI agent. It uses machine learning and natural language processing technology to answer users' questions and perform tasks, such as calling contacts stated in voice commands.

Autonomous vehicles can also be considered intelligent robotic agents. They use sensors, Global Positioning System navigation, weather and traffic conditions, and cameras for reactive decision-making in the real world to maneuver through traffic.

Tech vendors are developing autonomous AI agents that require less human intervention and oversight, as OpenAI's release of GPT-4 and GPT-5 demonstrated. A fully autonomous agent would be considered artificial general intelligence, where the AI agents are sentient and don't require guidance from human agents at all, but it's a theoretical concept and not yet in use.

Enterprise use cases

There are countless uses for intelligent agents across various industries and operations. A cross-section of uses for intelligent agents includes the following:

• Customer experience (CX). Intelligent agents can provide a foundation for many types of automated services, such as IT service requests, ticketing, password resets and account access. Human agents use intelligent agents to speed access to customer data and prior interactions that can enhance CX with current requests.
• Human resources (HR). Intelligent agents can streamline routine HR processes, such as employee onboarding or offboarding. Agents can also automate the collection and processing of HR forms, help employees access HR policies and procedures, such as the vacation scheduling process, and answer questions about benefits.
• Finance. Intelligent agents can process and analyze vast amounts of data to assist with financial tasks, such as fraud detection and financial compliance. Agents can assist with completing and filing tax and other business documents, aid employees in expense submissions and reconcile invoices.
• Procurement. Intelligent agents can enhance routine procurement processes, managing vendor agreements, contracts and licenses. Agents can help with logistics and supply chain management tasks as well.
• Sales and Marketing. Intelligent agents can help sales staff schedule calls and manage lead and account information through customer relationship management platforms to ensure that sales staff can access leads quickly and accurately. Agents also use account histories and purchasing patterns to create targeted sales campaigns and perform customer satisfaction analytics.
• Operations. Intelligent agents can help with different backend tasks, such as generating documents and reports, and analyzing data to provide business insights. They can identify potential risks, automate complex business workflows, flag suspicious network and system activities, and provide predictive maintenance for manufacturing equipment or vehicle fleets.

Benefits and challenges of intelligent agents

Intelligent agents are powerful and complex AI entities capable of providing value to the enterprise. However, there are technical and business issues to consider before building and deploying agents. Some major benefits of intelligent agents can include the following:

• Better efficiency. Intelligent agents automate routine or repetitive tasks, speeding business processes that are faster and more accurate, compliant and consistent. This also lets human employees focus on more creative or strategic tasks.
• Fewer errors. The consistency provided by intelligent agents can reduce the oversights and errors encountered with human interaction. This is important with complex, error-prone tasks that can have a significant effect on the business.
• Data-driven decisions. Business leaders want help making the best decisions, but it's easy to overlook, forget or dismiss data. Intelligent agents incorporate vast amounts of seemingly unrelated data to provide insights that humans might not recognize. This can yield faster and more accurate business decision-making.
• More availability. Agents can operate 24/7, enabling continuous operation for some tasks, such as customer service, on a global scale.
• Cost savings. The efficiencies and capabilities intelligent agents provide can reduce business expenses, as well as reduce the costs associated with human oversights and mistakes.

Despite the benefits, intelligent agents can pose several formidable challenges that must be considered. The following are some of the challenges involved with intelligent agents:

• Design complexity. Intelligent agents can be complex and costly to build and deploy. The design complexity and the nuances involved in their training add a level of uncertainty to their performance and reliability that can trouble business leaders.
• Integration complexity. Intelligent agents must be able to access an assortment of data sources, applications and systems. It can be difficult to design an agent with the necessary integrations and update the agent as new integrations are needed. This can result in inefficient or problematic data flows.
• Potential bias. Intelligent agents are built from algorithms and trained on sample data. Machine learning bias in algorithms and poor-quality data, such as insufficient, incomplete or inaccurate data, can yield outcomes that are discriminatory or sub-optimal for some user groups. Bias mitigation and accuracy are critical for reliable AI operation, as well as appropriate business governance.
• Ethics and explainability. Organizations must ensure that intelligent agents are employed ethically -- accessing business data for acceptable business purposes -- and take concrete steps to prevent intelligent agents from being used improperly or illegally. Further, business governance often requires transparency into the agent's algorithms and training data, ensuring that intelligent agents perform and make decisions in ways that are predictable, repeatable and well-understood by business leaders and regulators.
• Security and data privacy. Intelligent agents can access and process enormous volumes of sensitive or personally identifiable information. This raises the risk of data breaches because the agents can provide attackers with new and unproven vectors of attack. Similarly, the ways that agents process, store and render data also carries data privacy risks that can violate compliance regulations.

The future of intelligent agents

Intelligent agents are evolving fast. They will become more autonomous and able to handle tasks faster and better. They will also get better at collaborating, integrating and learning. Some future developments to watch are expected to include:

• Greater autonomy. Automation isn't a new idea, and automated agents, such as chatbots, have been available for years. However, these older platforms relied on rules and guardrails, and often require constant human oversight. Future intelligent agents will be capable of startling autonomy and will learn, adapt and operate with greater levels of independence.
• Task complexity. Intelligent agents can parse tasks into smaller executable actions or steps. This trend will continue to enable agents to perform ever larger and more complex tasks without the need for specific programming or modifications. This, in turn, will further facilitate greater agent autonomy.
• Better collaboration. Agents are rarely ubiquitous and are often task specific. This requires multiple agents operating together to collaborate and share data to execute desirable actions. This capability will accelerate and offer highly specific and capable agents that are composed as needed to handle large, complex tasks.
• Better integration. Agents require access to data, networks and systems through varied integration mechanisms. But integration can limit the data, applications and systems that an agent can interact with. Integrations will improve, possibly giving rise to new AI agent integration standards. This will enable more data and systems to interoperate and support more complex problem-solving and decision-making capabilities.
• Enhanced learning. Intelligent agents can learn from their outcomes to identify errors and optimize results. This ability will also become refined over time, giving agents more seamless abilities to gauge outcomes and adjust behaviors with great autonomy.

Stephen J. Bigelow, senior technology editor at TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.