What Are AI Agents? A Complete Guide

An AI agent is a versatile tool designed to handle tasks and processes autonomously. Think of it as a highly intelligent assistant that streamlines workflows, using a set of high-tech processes to help you achieve your goals.

While chatbots or generative tools might come to mind when you think of AI agents, they go beyond simple language processing and image generation. They can make decisions, perform reactive actions, and interact with external systems, such as managing your calendar or communicating with customer service platforms.

Here’s a guide to what these agents are, how they operate, and what types of AI agents exist.

Components of an AI agent system

AI agents have different components that make up their body or software, each with its own capabilities.

• Sensors: These allow the agent to perceive its environment to gather information (inputs from the world: images, sounds, radio frequencies, etc.). These sensors can be cameras, microphones, or antennas, among other things. For software agents, this could be a web search function or a tool for reading PDF files.
• Actuators: These help the agent navigate the world. They can be wheels, robotic arms, or a tool for creating files on a computer.
• Processors, control systems, and decision-making mechanisms: These make up the agent’s “brain.” I’ve grouped them together because they share similar functions, but not all of them can be present in an AI agent system. They process information from sensors, devise the best course of action, and issue commands to actuators.
• Learning systems and knowledge bases: These store data that helps the AI ​​agent complete tasks; for example, a database of past facts or insights, difficulties encountered, and solutions found.

Since the shape of an AI agent depends largely on the tasks it performs, some AI agents may have all of these components while others don’t. For example, a smart thermostat may lack learning components, having only basic sensors, actuators, and a simple control system. A self-driving car has everything on this list: it needs sensors to see the road, actuators to navigate, decision-making for changing lanes, and a learning system to remember how to navigate the more complex parts of the city.

Types of AI agents

This list is constantly expanding, but here’s how I would currently divide the AI ​​agent landscape.

General AI Agents

• Simple reflex agents: They look for a stimulus on one or a small set of sensors. Once they detect that signal, they interpret it, make a decision, and produce an action or output. You can find these agents in simple digital thermostats or in the smart vacuum cleaner that’s currently scaring your dog.
• Model-based reflective agents: They maintain an active internal state, gathering information about how the world works and how your actions affect it. This helps improve decision-making over time. You’ll find them forecasting inventory needs in a warehouse or in the self-driving car now parking in front of your window.
• Goal-based agents: They create a strategy to solve a particular problem. They generate a list of tasks, take steps to solve it, and understand whether those actions bring them closer to the goal. You can find these agents defeating human chess champions and in AI agent applications, which I’ll discuss in a moment.
• Utility-based agents: They generate the possible decision outcomes in circumstances with many viable alternatives. They run each possibility and score it according to its utility function: Is the best option the cheapest? The fastest? The most efficient? This is very useful for helping to identify the ideal option and perhaps overcome episodes of human analysis paralysis. You can see them optimizing traffic in your city or recommending the best shows for you to watch on TV.
• Learning agents: As the name suggests, they learn from their environment and behavior. They use a problem generator to create tests and explore the world, and a performance element to make decisions and act on what they’ve learned so far. They also have an internal critic to compare actions taken against the observed impact on the world. These agents are preventing spam from reaching your inbox.

Agentive AI Chatbots

Model intelligence continues to increase dramatically, and frameworks for knowledge management and interactions with external systems are also constantly improving.

This takes the now-classic AI chatbots, like the 2023 version of ChatGPT, to a new level of functionality and power. Here are the main differences between the old chatbots and the new agentive chatbots:

• New agentive chatbots can connect to live knowledge bases, responding with contextually aware outputs, which improves response accuracy (recall-augmented generation, or RAG). Older chatbots relied solely on pre-trained experience.
• New agentive chatbots can more accurately detect user intent and trigger function calls, allowing them to interact with external systems. OpenAI introduced this functionality in June 2023, paving the way for web search, for example. Older chatbots couldn’t access external tools.
• Depending on the configuration, new agentive chatbots can take multiple turns responding to a request or achieving a goal, queuing to perform multiple actions if they decide this will increase the quality of the output. Older chatbots worked with a single-turn system, where you sent a message, the bot calculated the response, and returned it.

The platforms for building these agentive AI chatbots are accessible: non-technical users can create useful agentive AI chatbots without ever needing to look at Python or JavaScript.

Computer Usage Agents (CUA)

What if you had an LLM that could use your computer and do the boring tasks for you? Here it is: Computer Usage Agents combine the power of AI with knowledge of computer usage—typing, pointing and clicking, navigating websites, ordering Ubers, ordering pizza—and turn boring administration into accomplished administration.

Currently, there are two methods to implement CUA:

1. The first, and most risky, is to allow the LLM full access to your machine. They can do anything you can do on your computer, including making mistakes.
2. The second, and the best one so far, is to set up a virtualized machine, either locally or on a remote server, so that the AI ​​has its own separate space to amaze or disappoint without major consequences.

Multi-agent systems

And if you have a truly complex task to complete, such as running an entire business with AI, you can combine these agents into multi-agent systems. You can have one AI agent act as the control system, generating a list of tasks and delegating them to other specialized AI agents. As they complete these tasks, the output is stored and analyzed by an internal critic, and the entire system continues to iterate until a solution is found.

While there are developer-grade frameworks for building this (Microsoft’s AutoGen is one example), there are no viable no-code platforms for implementing it yet. But given that there’s still so much to learn about single-agent systems, it’s still too early to dive into this.

How does an AI agent work?

Traditional workflow of an AI agent

In short, an AI agent uses its sensors to collect data, control systems to think of hypotheses and solutions, actuators to perform actions in the real world, and a learning system to track its progress and learn from its mistakes.

Let’s dive into how a goal-based AI agent works, since you’ll likely build or use one of these in the future.

1. When you enter your goal, the AI ​​agent goes through goal initialization. It passes your request to the central AI model and returns the first output of its internal monologue, showing that it understands what it needs to do.
2. The next step is creating a to-do list. Based on your goal, you’ll generate a set of tasks and understand the order in which they should be completed. Once you decide you have a viable plan, you’ll begin researching information.
3. Since the agent can use a computer the same way you do, it can gather information from the internet. I’ve also seen some agents that can connect to other AI models or agents to outsource tasks and decisions, allowing them to access image generation, geographic data processing, or computer vision capabilities.
4. All data is stored and managed by the agent in their learning system/knowledge base, so they can share it with you and improve their strategy as they progress.
5. As tasks are checked off the list, the agent assesses how far away they still are from the goal, gathering feedback from both external sources and their internal monologue.
6. And until the goal is met, the agent will continue iterating, creating more tasks, gathering more information and feedback, and moving forward without stopping.

Workflow of agentive AI chatbots

Agentive AI chatbots are a little different in scope compared to the general definition of AI agents. Here’s how they work, based on the framework proposed in the OpenAI Assistants API.

1. You start a new thread for the agent. This will store all messages, called files, and the results of function calls.
2. You set a trigger action with the software: this can be based on a date and time combination, a change in a database or system, receiving a message, or a manual trigger.
3. Once triggered, the AI ​​model will analyze the request, interpret the intent behind it, and execute one or more actions to generate a response.

• If the model detects that the user wants to know about a specific topic, it can activate a file search tool. This will search a connected knowledge base for data related to the request.
• If the model detects that the user wants to interact with an external system—searching the web, searching an external database, writing a new page in Notion—it will instead initiate a function call and interact with the service’s API.
• If none of the above are detected, the model adds a message to the thread with its training database.

4. The software sends the agent’s response back to you, which could include a report of the steps taken, generated messages, document sources, or links to external URLs.

6 benefits of AI agents

Adopting AI agents offers numerous benefits, transforming the way businesses interact with their customers and manage their service operations.

1. Greater efficiency

AI agents can handle multiple customer interactions simultaneously, significantly reducing response times and increasing the efficiency of customer service operations. They are also able to identify whether to escalate to a human, selecting the representative with the most appropriate skill set to handle the query. This allows companies to handle a higher volume of queries without compromising service quality.

2. Improved customer satisfaction

AI agents provide fast and accurate responses, leading to higher customer satisfaction scores. They can use data to personalize interactions, improving the overall customer experience. And because they learn over time, they are geared toward continuous improvement.

3. 24/7 availability

AI agents are available 24/7, ensuring customer inquiries are addressed immediately, regardless of time zones or business hours. This continuous availability helps businesses meet customer expectations for self-service and improves customer loyalty.

4. Scalability

AI agents can easily scale to handle higher volumes of customer interactions, making them ideal for businesses looking to grow without compromising service quality. As case volume increases, AI agents can easily adjust to handle the additional load, ensuring consistent and reliable support.

5. Data-driven insights

AI agents generate valuable data about customer interactions, preferences, and behaviors. Companies can use this data to gain insights into customer needs and trends, enabling them to make informed decisions and improve their service offerings.

6. Consistency and precision

AI agents provide consistent and accurate responses to customer queries, reducing the risk of errors and ensuring customers receive reliable information. They can improve the accuracy of your responses through agentive loops and human-like reasoning. This consistency helps build trust and confidence in your brand, as customers get the experience they expect.