With global AI markets expected to reach $1.3 trillion by 2030, it’s clear that AI technology will have a prominent spot in modern business operations for the foreseeable future. Now the question is no longer whether to use AI, but rather what type of AI to use and businesses are increasingly turning to intelligent agentic AI solutions.
Given AI agents' abilities to automate more complicated tasks, streamline operations, and maximize user productivity, it’s not surprising that their popularity is quickly growing over standard AI solutions. One report found that over 50% of respondents are using some form of agents today. In this article, we'll explore what intelligent AI agents are, the different types of AI agents, and how they operate within a business context.
We'll also discuss real-world use cases and examples to illustrate how autonomous agents are transforming enterprise AI, along with the key benefits of integrating AI agents into your workflows. These include enhanced efficiency, improved employee and customer experiences, and superior data analysis and decision support, which is why intelligent AI agents are quickly becoming indispensable tools for modern businesses.
What are intelligent agents in AI?
Intelligent agents are AI systems that can interact with the world around them, learn, and work towards achieving goals, mimicking human behaviors and cognition.
Imagine a smart AI assistant that can understand your requests (like asking for the weather or ordering a pizza), learn your preferences, (it knows you like extra cheese on your pizza), and then take action on its own (like booking a ride or scheduling a meeting.
In other words, intelligent agents in AI can think and act somewhat independently, learn from its experiences and adapt to new situations, and aim to achieve specific goals, like helping you with a task.
These intelligent AI agents can take many forms across different interfaces, from voice assistants like Siri or Alexa, to smart chatbots or software programs. These AI agents are the building blocks of agentic AI.
Agentic AI understands natural language queries to anticipate user needs and proactively determine the steps required to help users achieve their goals. This capability is particularly valuable for complex, multi-step tasks such as creating user accounts, changing passwords, and retrieving purchase order details. By moving past reactive interactions, an agentic AI solution becomes a collaborative partner, or AI copilot, driving meaningful results.
Intelligent agents, as the name suggests, are a more advanced form of AI. Intelligent agents learn from their actions, make decisions without explicit instructions, and can handle a wider range of business use cases where most traditional AI tools fall short.
How AI agents can work autonomously
You're likely familiar with generative AI tools, chatbots, or smart assistants that handle tasks such as answering queries, providing recommendations, and managing schedules. However, AI agents work differently; they are designed to autonomously perform complex, multi-step tasks with minimal oversight.
Intelligent agents in AI are advancing at such a pace that they are operating independently in many business scenarios, driving productivity and accelerating automation in the workplace. According to Gartner’s Intelligent Agent in AI report, 33% of enterprise software applications are expected to include agentic AI by 2028, enabling 15% of daily work decisions to be made autonomously. Unlike current AI models that require prompts, agentic AI can create plans, use tools, and produce dynamic outputs without explicit inputs.
It's important to understand that while generative AI tools react to user inputs within predefined parameters, AI agents are capable of recognizing their environment, making decisions, and taking proactive actions to achieve user-defined goals. How do they do this?
Fundamentally, AI agents function using two primary components Architecture and Agent program:
- Architecture: AI agents rely on an underlying framework consisting of AI-enabled devices, databases, and connected networks for communication. This framework is essential for their operation, similar to how hardware and software are vital for business systems.
- Agent program: AI agents rely on an underlying framework consisting of AI-enabled devices, databases, and connected networks for communication. This framework is essential for their operation, similar to how hardware and software are vital for business systems.
Agentic AI solutions leverage large language models (LLMs) to recognize the context behind the information they absorb. By understanding this vital component, AI agents can reason about the data they collect just like human agents would.
With the ability to seamlessly connect to different AI systems and use natural language processing (NLP) algorithms to learn and adapt over time, agentic AI takes business automation to a completely new level.
The 5 Types of Intelligent AI agents
Before getting started with intelligent AI agents in your organization, it’s important to understand the wide range of intelligent agent types to consider when creating new automated workflows, each designed to help address different business goals and objectives.
Goal-based AI agents
A goal-based agent works toward specific goals or objectives by analyzing an available knowledge base with predefined rules that help direct its actions.
When given a direction, goal-based agents will respond to changes in the environments they operate in while finding the most efficient path to achieve a task. An example of a goal-based agent is modern navigation systems that deploy smart route calculations for self-driving cars.
As traffic patterns change in real time, these goal-based agents will calculate new optimal routes by first analyzing multiple available routes. After factoring in potential delays caused by traffic accidents, time of day, or ongoing construction, the goal-based agent will present the route with the best arrival time.
Learning agents
Learning agents perform specific tasks while learning from and improving their output over time. Unlike other AI agents that require and are constrained by predefined rules, learning agents are highly adaptable.
Multiple components come together to power a learning agent, including:
- A large enough dataset to capture the details of the problem it's looking to solve
- Deep machine learning that allows for data pattern extraction
- Various metrics to benchmark performance improvements over time
- A feedback mechanism that will help the agent refine its output
A good working example of a learning agent is streaming service companies that use recommendation engines.
Platforms like Netflix and YouTube provide their users with recommendations based on their previously viewed videos. These engines leverage learning agents that analyze the content categories, trending search topics, and individual user tendencies to calculate the probability of users choosing other similar content to watch.
Utility-based agents
Utility-based agents are similar to goal-based agents, but they do have some key differences. While goal-based agents have set objectives, utility-based agents have more flexible parameters. Their primary responsibility is to maximize the efficiency of the utility function used to achieve those goals.
For example, smart thermostat systems use utility-based agents to help maintain the most optimal temperature setting in a home. However, this involves more than just lowering the thermostat when it’s too hot and increasing it when it’s too cold.
Utility-based agents in smart thermostats use multiple sensors to measure outside temperatures, humidity levels, and usage patterns. This added intelligence allows them to pre-heat or cool a home to maximize energy efficiency without human intervention.
Reflex agents
As the name suggests, reflex agents respond to current situations in the environment in which they operate. These are the simplest forms of intelligent AI and operate using a simple “if-then” formula—if “this type of action” happens, then “do this.”
Unlike other AI types, reflex agents won’t consider previous or predicted actions. Since they don’t use any past interactions to drive their responses, they’re considered “stateless” and have a limited scope of functionality.
A primary example of simple reflex agents is email spam filters and other basic system automation. In these scenarios, a reflex agent will respond once an action triggers, such as receiving an email from an untrusted source. Once this occurs, the reflex agent will carry out a preassigned task without any further analysis.
Hierarchical agents
Hierarchical agents break up larger tasks into small sub-tasks. Like many businesses that structure their organizations into different departments, hierarchical agents delegate responsibilities from the top down.
To handle more complex tasks, hierarchical agents use “supervisors” or “orchestrators” to segment larger tasks into smaller segments and distribute them to other AI agents. Each agent will communicate their status with one other while working seamlessly together to achieve a specific objective.
A good example of a hierarchical agent is in an autonomous drone delivery system. To achieve this highly advanced automation, delivery assignments use multiple AI agents simultaneously. Top-level agents oversee the delivery status of an order, while other “worker” agents perform different steps of the process.
For example, one agent function creates an optimal route, while another detects and avoids obstacles. A packaging agent will securely load, transport, and deliver the package, and another software agent communicates the drone’s mechanical properties.
All of these features come together to create one fully autonomous solution.
Learn more about AI agents and how they perform in different business settings with our Ultimate Guide to AI Agents.
3 key benefits of intelligent agents
Intelligent AI agents can provide a variety of benefits to organizations. Whether you’re looking for more ways to automate workflows, improve employee and customer experiences, or drive better decision-making, intelligent AI agents can assist.
1. Efficiency and automation
Intelligent AI agents blend seamlessly into your current business systems and support processes by autonomously managing complex, multi-step processes with minimal oversight. Thanks to their wide range of use cases, enterprises can use AI agents to automate any number of business processes, freeing up their employees to focus on more critical tasks.
Unlike other AI-driven solutions, intelligent AI agents can autonomously adapt and improve their efficiency over time. By dynamically adjusting their actions based on real-time data and internal feedback mechanisms, they enable organizations to deploy “hands-off” business automation solutions.
2. Improved employee and customer experiences
While automating different business elements is helpful, providing more seamless experiences for users is equally important. Intelligent AI agents can provide proactive, personalized always-on support solutions via smart personal assistants, chatbots, AI copilots, and other self-service tools. This eliminates the need for users to wait until the next business day to get answers to their questions or help resolving issues.
Employees also benefit from AI agent integrations through access to personalized business support intelligence and more streamlined workflows. One of the most common and easy ways for employees to leverage AI agents is with an agentic AI copilot. With AI copilots, employees can create automated reports, get answers to questions regarding company policies, or quickly access relevant information spread across multiple business systems and databases.
3. Data analysis and decision support
Many businesses dedicate significant resources to sifting through large datasets and creating complicated spreadsheets to gain important business insights. However, intelligent agents can excel in data analysis and decision support by autonomously interpreting complex data sets and generating actionable insights in real-time. With these intelligent AI agents, this process can be significantly more efficient and cost-effective.
Intelligent AI agents are far more capable of analyzing large amounts of data quickly and accurately, identifying trends and patterns that many human analysts miss. These agents can also cross-reference multiple database sources simultaneously, providing highly objective analyses for accurate decision-making processes.
Intelligent agents business use cases and examples
Many businesses have already begun using intelligent AI agents in their daily workflows. Regardless of your business’s size, there are several ways you can improve or completely automate various elements of your operations. Some common business use cases of intelligent agents include:
- Customer service: Businesses can leverage agentic AI-powered chatbots to not only respond to but also intelligently resolve customer issues. By leveraging previously gathered FAQs and past customer interactions, AI agents can provide highly valuable extended support services to busy organizations.
- Human resources: Intelligent AI agents can streamline various HR processes, such as personalizing onboarding of new staff members, managing employee benefits administration, and guiding employee training workflows. This intelligent automation reduces strain on HR teams, allowing them to focus on providing more personalized support to employees.
- IT support: Intelligent AI agents help to reduce costly IT backlogs by offloading repetitive tasks and processes to AI-powered automation. Intelligent AI agents can troubleshoot issues and complete complex tasks in real time on behalf of IT teams, providing employees or customers with immediate resolutions to technical issues, software updates, password resets, or device configuration.
A great real-world example of the power of agentic AI is Broadcom’s successful implementation of Moveworks’ AI Copilot, which the organization named 1.Bot.
Broadcom experienced exponential growth that challenged its ability to handle necessary IT operations. By integrating its infrastructure with Moveworks, the organization achieved an impressive result of resolving 57% of all employee IT issues within just 60 seconds.
Learn how autonomous agents are transforming enterprise AI
Intelligent AI agents create nearly limitless opportunities for organizations to optimize their business workloads. By leveraging advanced technologies like NLP and LLMs, intelligent AI agents can completely automate an extensive number of critical business functions.
Moveworks makes it simple to harness the full power of intelligent AI agents and agentic AI across your entire organization easily with Moveworks Copilot or you can use our platform to plan out your AI Agent builder and roadmap. Our agentic AI copilot is built on an Agentic Automation Engine, enabling Moveworks to leverage the full capabilities of intelligent agents by creating a seamless environment for enterprises to build and execute their AI automations.
By connecting multiple AI agents across entire IT ecosystems, enterprises can experience a new level of AI automation that drives better business efficiency and more cost-effective operations.
Learn more about agentic AI solutions and the real value they can bring to your business.
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