While the concept of artificial intelligence has been around for decades – and its use has been widespread in various basic forms for years – the release of ChatGPT in November 2022 was the first time the world caught a glimpse of the promise of large language models (LLMs). 

AI agents – particularly the new crop of such agents, based on very large machine learning (ML) models – take us a step further towards that reality. With these agents being one of the areas of most interest to AI vendors and researchers, their promise and potential to transform how we view AI and technology as a whole is now being realized. 

With that in mind, this guide explores the concept of AI agents, how they work, their benefits and potential applications, and possible future trends for this exciting new application of the latest AI technology. 

What Is an AI Agent?

Sometimes also referred to as autonomous AI agents, an AI agent is an application or system capable of executing a given task without ongoing direct human intervention. When assigned an objective, an AI agent will perceive its environment, assess the tools at its disposal, and formulate a plan to achieve its given goal. 

As examples, in a professional setting, you could instruct an AI agent to find a list of suppliers, email them for quotes, and sort replies according to the best price. In a personal setting, you could instruct an AI agent to create a shopping list based on a recipe, purchase the ingredients online, and have them delivered to you.  

How Does an AI Agent Work?

In essence, an AI agent works through the process of assigning, creating, or inferring an objective, which it will then break down into a series of tasks and attempt to complete. This process can be divided into three stages: task definition and planning, decision-making, and feedback and adaptation. 

  • Task Definition and Planning
    • Define and assign an objective: giving an agent a predefined goal you want it to accomplish.
    • Assign Resources: select the tools and sources of information the agent will be permitted to use to achieve its goal.  
    • Environmental assessment: the agent uses sensors and any other available data sources to collect information about its environment; this helps it understand its given task in greater context, as well as potential obstacles.
    • Plan generation: taking the tools at its disposal into account, the agent devises strategies for achieving its goal, which typically involves breaking down its tasks into subtasks, and potentially also breaking the goal down into subgoals.   
  • Decision-Making
    • Data analysis: analyzing available data – such as environmental sensor readings, past experiences, and the model that powers it, to predict the outcomes of prospective actions it could take.
    • Action execution: the agent sequentially selects and executes the action it has determined will maximize the likelihood of success. 
  • Feedback and Adaptation
    • Performance Monitoring: the agent monitors the outcome of its actions and evaluates if they brought it closer to accomplishing the given objective. 
    • Feedback loop: The agent uses the feedback to adjust its strategy and potential actions to take. If permitted, i.e., if programmed to do so, it can ask for human intervention if it is stuck on a task. 
    • Adaptation and learning: The agent continuously learns from its experiences. It monitors the results of its actions and updates its knowledge base and decision-making processes based on the new information.

The Components of an AI agent

  • AI Model: an AI model: recent advances such as an LLM, VLM (vision-language model), or, more recently, LMM (large multi-modal model) could be used as the core of an AI agent, acting as its decision-making mechanism. The model will process data collected by sensors, make decisions based on said data, and take actions to achieve the agent’s goals.
  • Sensors: components responsible for collecting data from the agent’s environment so it can “perceive” it accurately and act accordingly. Sensors are an agent’s input devices, enabling it to learn about the world. In software agents, a sensor would be digital interfaces to websites or databases, while in a robotic agent, these include cameras and microphones. 
  • Actuators: these are an agent’s means of output – enabling it to take actions based on its given objective and data collected from its sensors. In software agents, these are components that can control other applications or devices, while for a robotic agent, these could be appendages, i.e., limbs, display screens, or speakers. 

Multi-Agent Systems

A multi-agent system (MAS) is a framework that allows multiple AI agents to collaborate to solve problems and achieve predetermined goals. They offer several benefits over systems consisting of a single AI agent, with the major one being that they are capable of taking on more complex tasks. 

A key reason for this is that agents can learn from the behavior of other agents as well as their environment. Better still, an MAS is scalable – additional agents can be instantiated if the existing system isn’t up to the task, or can’t cope with growing demand.   

Secondly, multi-agent systems are more fault-tolerant to individual agent failures than single-agent systems. This offers higher availability, which is desirable if the agent-powered system represents a critical function. 

Finally, a novel way in which an MAS can achieve its given goal is to have agents cooperate to reach a desired objective. This could involve agents sharing information about the measures they have taken so far, so other agents avoid wasting effort. 

What Are the Benefits of AI Agents?

One of the reasons AI agents are increasing in use is the large range of advantages they offer; let’s look into each in more detail. 

  • Efficient Automation: AI agents can take on repetitive tasks such as FAQs, common requests, batch jobs, etc., without the need for human involvement. This frees up employees to work on more rewarding and value-adding activities. 
  • Improved Decision-Making: agents can analyze vast amounts of data faster and more accurately than a person can, allowing for better data-driven decision-making. 
  • Reduced Human Error: subsequently, the ability to process large amounts of data with greater accuracy means work carried out by agents does not suffer from the mistakes made by humans. 
  • Increased Availability: AI agents are scalable and deployable around the clock. This ensures that services or support are available any time a user requires it. 
  • Increased Safety: autonomous agents can be deployed in dangerous environments, replacing the need for humans and eliminating the risk of injury or catastrophic loss as a result. 
  • Cost Savings:as a result of the automation offered by agents, the workforce is freed from the burden of mundane work, boosting their productivity and optimizing labor costs. 
  • Scalability: As your user base or data grows, more agents can easily be deployed at scale to meet demands.

Types of AI Agents

Simple Reflex Agents

Also known as rule-based agents, these are the most basic type of AI agent and follow a collection of rules that specify an action to perform for a particular predefined condition or “trigger”. Simple reflex agents make decisions based on the current data from their sensors without memory or the capacity to learn. 

Model-Based Reflex Agents

A model-based reflex agent maintains an internal state that represents aspects of the world, so that it can use its past experiences to make decisions. While such agents still rely on predefined condition-action rules, like simple reflex agents, they can use learned information to make decisions – making them more versatile and capable of operating within more unpredictable environments. 

Goal-Based Agents

Goal-based agents maintain a goal/objective description, and devise strategies to achieve it, evaluating their current state based on their objective and selecting actions that will facilitate the completion of the goal conditions. Since goal-based agents are adaptable, they are well-suited to more intricate or dynamic environments. 

‍Utility-Based Agents

This type of agent is designed to choose the set of actions that maximizes a defined utility or reward. Subsequently, utility-based agents decide which actions to take in accordance with their objective and the environment, but also based on a reward function that quantifies the desirability of different outcomes or “states”. 

This makes utility-based agents suitable for use cases where multiple goals are in competition and the relative importance of each goal must be considered. An example of this would be an AI-powered investment portfolio application that must account for factors like return, risk, and liquidity when executing trades.

Multi-Modal Agents

Emerging alongside large multi-modal models (LMMs), multi-modal agents are capable of autonomously carrying out tasks that require a variety of multiple modalities, i.e., text, audio, images, etc. Multi-modal agents have the means to process multiple forms of input, enabling them to perceive their given environment more accurately than other types of agents. Consequently, they can be applied to a wider number of use cases because they are able to utilize more tools and resources. 

Applications of AI Agents

Let us turn our attention to some of the use cases to which AI agents are currently being applied.  

  • Virtual Assistants: services like Siri, Alexa, and Cortana are actually AI agents that are capable of understanding natural language commands in real-time and performing a wide variety of tasks like researching the answers to questions, ordering items, and controlling internet-connected smart devices.
  • Customer Service Chatbots: used by organizations to automate customer service interactions. In addition to answering common questions, agents reroute queries and escalate more involved issues to human personnel.
  • Recommendation Engines: AI agents can learn user preferences from past behavior to offer personalized recommendations for products, services, or content. Streaming services like Netflix and eCommerce platforms like Amazon are prominent examples of this. 
  • E-Learning Agents: these deliver educational content based on a student’s particular competence and rate of progress. For instance, they will provide additional resources for areas the student finds challenging while skipping over familiar material.
  • Data Analysis and Forecasting: agents can process and interpret vast datasets to identify patterns and anomalies in an effort to predict future events. This is ideally suited for the financial industry that uses such predictive analysis to identify stock market trends, fluctuations, and potential risks – to optimize investment strategies. 
  • Real-time Cybersecurity Monitoring and Alerting: AI agents are capable of monitoring IT infrastructure and spotting potential security breaches around the clock – and with far more accuracy than any single human can.  
  • Diagnosis and Treatment Agents: an agent’s ability to process massive amounts of data and detect patterns is also useful in the medical field. AI agents can help identify medical conditions from a patient’s data – particularly when it comes to analyzing patterns in images as well as in large amounts of suitably anonymized data (across patients). 
  • Robotics: self-driving vehicles and autonomous drones are examples of robotic agents that use sensors and actuators to interact with physical environments. Meanwhile, in sectors such as large-scale manufacturing, industrial robots are used as agents to perform work such as assembly and welding. 
  • Infrastructure and System Monitoring: AI agents help in overseeing industrial infrastructure and systems, detecting anomalies in equipment that could signal an impending failure. 
  • Gaming: Non-playing characters (NPCs) in video games are a basic form of agent that simply responds to a user’s actions. However, more advanced agents can add realism to games: a trend that will become more prevalent as we move into Virtual and Augmented Reality environments.

Future Trends in AI Agents

To finish, let us briefly explore a few of the likely advancements and trajectories of AI agents as we head into the future.  

Increased Integration and Prominence Across a Variety of Industries

First and foremost, the use of AI agents will become increasingly widespread as they become more accessible and better understood by organizations.  Additionally, as research into the applications of autonomous agents continues and their capabilities increase, they will play a larger role in an array of sectors. This includes: 

  • Healthcare: agents will assist medical professionals in diagnosis, treatment, and even surgery.
  • Transportation: self-driving vehicles and autonomous drones will become more common.
  • Manufacturing: AI agents will manage entire facilities – including the operation and maintenance of machinery. 
  • Customer service: AI agents will handle most customer service inquiries, making cheap and efficient 24/7 support the status quo. 

Less Use of Individual Applications

Presently, with AI agents in their infancy, we still have to consider which tools and resources to give an agent access to for it to be able to complete its given objective. In the future, however, this may not be the case – as agents will be advanced enough to incorporate a wide variety of common applications in a standardized fashion – perhaps in an “agent app store”. As a result, we may be able to entrust an agent with an objective, composed of a series of tasks, without having to explicitly specify the actual agents or tools required for the task. In light of this, it is entirely possible that people will stop thinking in terms of apps – and will instead think of  agents and assistants. 

More Advanced AI Agents

Inevitably, just like past AI techniques systems, autonomous agents will become far more powerful and sophisticated – distinguished by their ability to learn and adapt in real time. In particular, there are two additional evolutionary leaps that autonomous agents will most likely make:

  • Theory of Mind: this will lead to agents possessing a level of cognitive understanding, with the ability to interpret emotions and intentions – in both people and other agents. This will create more authentic human-agent interactions, in which an agent can tell if a user is frustrated, happy, angry, confused, etc., and match their conversational style  – or make recommendations to their mood.
  • Self-Aware Agents: the apex of autonomy, in which an agent understands its environment and is self-aware – enabling it to reflect on its capabilities, assess challenges, and consider what it might need to complete the task at hand. 

Agent Swarms

Similar to the concept of containers in application development, an agent swarm enables the rapid automated deployment of multiple agents. In such a swarm, AI agents will be capable of activating additional other agents – of various types most suited to the given objective, assign them tasks, coordinate their activity, and monitor their progress. 


It is no exaggeration to state that AI agents are poised to be a revolutionary technology that will expedite the further adoption of AI technology. A key reason for this is that, as a concept, they are easy to understand – abstracting many of the complexities of AI and ML models from the user. Moreover, the multitude of benefits they offer, such as increased productivity and cost savings, is something stakeholders in every industry can understand. 

However, autonomous AI agents can also be a sobering and even frightening prospect. By streamlining so many tasks, jobs previously held by humans may become obsolete – necessitating labor reshuffling or employee reskilling. More importantly, the ethical and security concerns that become increasingly apparent as AI agents grow in sophistication is something that will have to be addressed by AI vendors and researchers, key stakeholders from every industry, and even governments. 

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Kartik Talamadupula
Director of AI Research

Kartik Talamadupula is a research scientist who has spent over a decade applying AI techniques to business problems in automation, human-AI collaboration, and NLP.