Welcome to the AI Agents Era: what they are and why they are changing everything

Much has been said about generative artificial intelligence, which, through its application in tools, assists us in performing various tasks in our daily lives. But, imagine that this AI goes one step further: not only does it act through tools, but it becomes a “brain” capable of interacting with its environment to gather and analyze data that it will then use to execute tasks that meet previously established objectives.

Well, that is what artificial intelligence agents do, a concept that is increasingly present in the technological field and still has much room for development. In this article, we will address all the questions you have about them: what they are, what they are used for, and how you can create one.

What artificial intelligence agents are

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

Although human beings establish these objectives, the agent independently chooses the necessary actions 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 environmental perceptions.

For example, an AI agent in a customer service center can manage inquiries without human intervention. It formulates questions for the user, searches information in internal databases, and provides answers. Based on customer interactions, the agent can determine if it can resolve the inquiry or if it is necessary to escalate the case 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: act based on environmental stimuli, without memory or planning. Example: game algorithms like Deep Blue in chess.
• Model-based agents: have a representation of the world and make decisions based on past experiences. Example: autonomous vehicles that analyze their environment and make decisions in real-time.
• Goal-based agents: not only react but pursue a specific objective by optimizing actions to achieve it. Example: recommendation engines like those of Netflix or Amazon.
• Utility-based agents: evaluate multiple options to choose the best one according to a utility function. Example: algorithmic trading systems that maximize profits in the stock market.
• Learning agents: use machine learning to improve with experience. Example: advanced chatbots like ChatGPT, which learn from user feedback.
• Multi-agent 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. Example: generative AI models like DALL·E (for images) or Claude (for texts).

How an AI agent works

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

1. Goal setting: the user defines a purpose for the agent, which breaks down the objective into specific subtasks.
2. Information acquisition: to execute its tasks, the agent needs data. It can acquire it from internal databases, online sources, or via interaction with other AI systems.
3. Task execution: with the collected information, the agent completes the tasks in logical order, continuously assessing whether objectives have been achieved. If necessary, it generates new tasks to optimize outcomes.
4. Learning and improvement: some agents have memory and adjust their responses based on past interactions.

Anyone with knowledge in programming, data science, and artificial intelligence can develop AI agents. However, their complexity varies, and there are tools that facilitate their creation even without advanced experience. ChatGPT API, Google Bard, Rasa, Dialogflow, or IBM Watson allow creating agents without the need to program from scratch. They also offer graphic interfaces and pre-trained models that ease implementation.

Once the AI agent is developed, it requires a suitable environment to operate. For this, it is crucial to decide 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. It is useful for testing and training the model before deployment.
• In the cloud: using services like Google Colab, AWS SageMaker, or Azure Machine Learning. It 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:

• Own server (on-premise): hosted in a private infrastructure within a company. This provides greater control and security but requires more maintenance. Example: A Linux server with Apache or Nginx.
• Cloud-hosted server: services like 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 the application’s code, libraries, and configurations, ensuring it always runs the same way, regardless of the operating system. This technology allows packaging an application with all its dependencies to run it later in any environment.
• External API (if the agent uses third-party AI models): instead of hosting it, use APIs like OpenAI, Google AI, Hugging Face, or IBM Watson. In this case, there is no need to train models, just send queries and receive responses.

Benefits of AI agents and examples of use

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

Moreover, process automation with AI contributes to cost reduction by minimizing human errors, optimizing operational efficiency, and eliminating unnecessary expenses resulting from inefficient manual processes.

Another key benefit is the improvement in decision-making, as thanks to machine learning, AI agents can process large volumes of data in real-time. This allows generating more precise analyses and providing relevant information that facilitates planning and the definition of business strategies.

Lastly, the implementation of AI in customer interaction enables personalizing recommendations, speeding up response times, and optimizing the user experience. This not only enhances customer satisfaction but also increases loyalty and conversion, strengthening the relationship between the company and its target audience.

These are some specific examples of AI agents in action:

AgentForce by Salesforce: an autonomous assistant for sales and customer service

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

Manus AI: the Chinese AI that simulates a human investor in the stock market

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

Klarna AI Agent: customer service without human intervention

The Klarna AI agent has revolutionized customer service in the financial sector, handling over 60% of inquiries without human intervention. This agent takes care of answering questions about payments, deadlines, and transactions, offering immediate solutions without needing to escalate cases to a human operator. Furthermore, it can analyze the customer’s tone and adjust its language to provide more empathetic responses, enhancing the user experience.

Tesla Autopilot: an AI agent for autonomous driving

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

Photo: ChatGPT