Welcome to the age of AI agents: what they are and why they’re changing everything

Discover how these systems, designed to perceive their environment, process information, and make decisions autonomously, work.

Much has been said about generative artificial intelligence, which, through its application in tools, helps us perform various tasks in our daily lives. But imagine if this AI went a step further: it not only acts through tools, but becomes a “brain” capable of interacting with its environment to collect and analyze data, which it then uses to execute tasks that meet previously established objectives.

Well, that’s what artificial intelligence agents do, a concept that’s increasingly present in the technological field and still has plenty of room for development. In this article, we’ll answer all your questions about them: what they are, what they’re used for, and how you can create one.

What are artificial intelligence agents?

Artificial intelligence agents are systems designed to perceive their environment, process information, and make decisions autonomously to accomplish a specific goal. These agents can operate at various levels of complexity, from virtual assistants to advanced robotics and business intelligence systems.

Although humans set these goals, the agent independently chooses the actions necessary to achieve them. While many software programs can execute tasks autonomously, AI agents are distinguished by their ability to make rational decisions based on data and perceptions of the environment.

For example, an AI agent in a customer service center can handle inquiries without human intervention. It does this by asking the user questions, searching internal databases for information, and providing answers. Based on interactions with the customer, the agent can determine whether they can resolve the inquiry or whether the case needs to be escalated to a human agent.

Another example could be an autonomous vehicle that uses sensors to detect obstacles and adjust its trajectory in real time.

Types of AI agents

AI agents can be classified according to their level of autonomy :

• Reactive agents: These act based on environmental stimuli, without memory or planning. For example, algorithms for games like Deep Blue’s chess algorithm.
• Model-based agents: They have a representation of the world and make decisions based on previous experiences. Example: autonomous vehicles that analyze their environment and make decisions in real time.
• Goal-based agents: They don’t just react, they pursue a specific goal, optimizing actions to achieve it. For example, recommendation engines like those of Netflix or Amazon.
• Utility-based agents: They evaluate multiple options to choose the best one based on a utility function. Example: algorithmic trading systems that maximize profits in the stock market.
• Learning agents: These use machine learning to improve with experience. For example, advanced chatbots like ChatGPT learn from user feedback.
• Multi-agent agents: Systems composed of several agents that collaborate or compete with each other to solve complex tasks. Example: intelligent traffic systems, where each autonomous car interacts with others.
• Generative autonomous agents: capable of generating original content or adapting to multiple tasks. Examples include generative AI models like DALL·E (for images) or Claude (for text).

How an AI agent works

Artificial intelligence agents operate through a continuous cycle of perception, processing, decision-making, and action, adapting to their environment to achieve a goal. Their operation is based on this structured flow:

1. Goal setting: The user defines a purpose for the agent, who then breaks the goal down into concrete subtasks.
2. Information acquisition: To perform its tasks, the agent needs data. It can obtain it from internal databases, online sources, or through interaction with other AI systems.
3. Task execution: Using the information gathered, the agent completes tasks in a logical order, continuously assessing whether the objectives have been achieved. If necessary, it generates new tasks to optimize results.
4. Learning and improvement: Some agents have memory and adjust their responses based on past interactions.

Anyone with programming, data science, and artificial intelligence skills can develop AI agents. However, their complexity varies, and there are tools that make them easy to create even without advanced experience. ChatGPT API, Google Play, Rasa, Dialogflow, and IBM Watson allow you to create agents without having to program from scratch. They also offer graphical interfaces and pre-trained models that make implementation easier.

Once the AI ​​agent is developed, it needs a suitable environment to run. Key to this is deciding where it will be hosted and how it will be accessed.

Where AI agents are created and hosted

The development of the AI ​​agent can be done in different environments, depending on the tools and languages ​​used:

• Locally on my computer: A development environment like Jupyter Notebook, VS Code, or PyCharm. This is useful for testing and training the model before deploying it.
• In the cloud: Using services like Google Colab, AWS SageMaker, or Azure Machine Learning. This allows access to GPUs or TPUs to train models faster.
• On a development server.

Once the agent is ready, it must be hosted on a server so that other systems can use it. There are several options:

• On-premise server: Hosted on a company’s private infrastructure. This provides greater control and security, but requires more maintenance. Example: A Linux server with Apache or Nginx.
• Cloud hosting: Services such as AWS, Google Cloud, Azure, DigitalOcean, or Heroku. This option offers scalability and less maintenance.
• Containers (Docker and Kubernetes): A container is a lightweight package that includes an application’s code, libraries, and configurations, ensuring that it always runs the same way, regardless of the operating system. This technology allows you to package an application along with all its dependencies and then run it in any environment.
• External API (if the agent uses third-party AI models): Instead of hosting it, I use APIs like OpenAI, Google AI, Hugging Face, or IBM Watson. In this case, I don’t need to train models; I just send queries and receive responses.

Benefits of AI agents and use cases

The incorporation of artificial intelligence agents into business operations brings numerous benefits. One of the most significant is increased productivity, as these systems can handle repetitive tasks, freeing up human teams to focus on strategic or creative activities that generate greater value for the organization.

In addition, AI-powered process automation contributes to cost reduction by minimizing human error, optimizing operational efficiency, and eliminating unnecessary expenses resulting from ineffective manual processes.

Another key benefit is improved decision-making, as machine learning enables AI agents to process large volumes of data in real time. This enables more precise analyses and provides relevant insights that facilitate planning and business strategy definition.

Finally, implementing AI in customer interactions allows for personalized recommendations, faster response times, and optimized user experience. This not only improves customer satisfaction but also increases loyalty and conversion, strengthening the relationship between the company and its target audience.

Here are some concrete examples of AI agents in action:

Salesforce AgentForce: A Self-Employed Sales and Customer Service Assistant

Salesforce has developed AgentForce, an artificial intelligence agent designed to automate sales and customer service processes. This system not only answers basic customer questions but also interacts with business tools to complete tasks such as scheduling meetings, following up on business opportunities, and updating databases in real time. Thanks to its integration with Salesforce Einstein 1, this agent is able to analyze data in context, providing accurate information to sales teams and allowing human agents to focus on higher-value, strategic tasks.

Manus AI: The Chinese AI that simulates a human stock market investor

Manus AI, developed in China, is an artificial intelligence agent specialized in financial decision-making. Its purpose is to simulate the behavior of a human investor in the stock markets, analyzing large volumes of data in real time and autonomously adjusting investment strategies. Manus AI uses advanced machine learning models to predict trends, identify opportunities, and minimize risks in the purchase and sale of financial assets. This technology has been designed to offer a more rational approach free from emotional biases, thus optimizing investment profitability in highly volatile environments.

Klarna AI Agent: Customer service without human intervention

Klarna’s AI agent has revolutionized customer service in the financial sector, handling more than 60% of inquiries without human intervention. This agent is responsible for answering questions about payments, deadlines, and transactions, offering immediate solutions without the need to escalate cases to a human operator. It is also capable of analyzing the customer’s tone and adjusting its language to provide more empathetic responses, improving the user experience.

Tesla Autopilot: An AI Agent for Self-Driving

Tesla Autopilot is an artificial intelligence agent designed to assist in driving electric vehicles. This system analyzes the environment in real time through cameras, sensors, and radars, allowing the car to perform maneuvers such as lane keeping, changing lanes, and adjusting speed based on traffic conditions. As more vehicles use this system, the agent learns from new situations, improving its decision-making capabilities and moving closer to fully autonomous driving.