AI Agents: The Future of Autonomous Software

Montevideo, May 2, 2025.

What if software could make decisions on its own, learn from its environment, and act without constant supervision? That is the future proposed by artificial intelligence agents, a new generation of systems capable of operating autonomously, adapting to change, and executing strategic actions in complex and dynamic environments.

AI agents are autonomous entities that not only respond to stimuli but also perceive, plan, and act according to specific objectives. These are models capable of making strategic decisions in real time, learning from context, and performing complex tasks without the need for constant human intervention.

The use of AI agents is transforming how software is developed. It’s not just about writing instructions anymore but having an active force that can collaborate, reason, and adapt. In an environment where efficiency, personalization, and scalability are essential, AI agents represent a natural and necessary evolution of intelligent software.

How do AI agents work?

An artificial intelligence agent is like a digital assistant capable of making decisions autonomously. Unlike traditional programs, which only execute specific instructions, an AI agent can:

• Observe what is happening around it (gather information).
• Reason about what it has observed (analyze data).
• Decide what to do (evaluate and select among different options).
• Act to achieve its goals (execute tasks).

AI agents have the ability to understand human language, learn from new situations, and use various tools to solve problems, similarly to how a person would. These tools can include web browsers, email services, databases, applications, or external systems, among others.

Currently, there is a standard protocol called MCP (Message Content Protocol) that allows agents to use tools in a unified way, facilitating their interoperability and expanding their capabilities.

A simplified architecture of an agent can be represented as follows:

To better illustrate the behavior of agents, Sofis Solutions presents a simple case called “The umbrella case” with the aim of clarifying these concepts.

How an AI agent learns: The umbrella case

Imagine an intelligent assistant that accompanies you in your daily routine. How does it learn to anticipate your needs? Sofis Solutions presents it in the following simple example:

1. Observation: The agent has access to the weather forecast and a camera at your house entrance.

   • Day 1 (rain): observes that you take an umbrella when leaving. 

   • Day 2 (sunny): observes you leave without an umbrella. 

   • Day 3 (rain): again, observes that you take an umbrella. 

2. Learning: the agent processes these observations and stores them in its memory. Upon detecting a consistent pattern, it generates a rule: “When it rains, this person takes an umbrella.”
3. Intelligent action: Day 10: The forecast indicates rain for tomorrow. 

   • The agent consults its knowledge base. 

   • Finds the learned rule about your habits. 

   • Sends you a notification: “It will rain tomorrow. Remember to take your umbrella.”

4. Continuous improvement: each time the agent observes your reactions to its suggestions, it continues learning and refining its rules. For example, if one day it rains lightly and you decide not to take an umbrella, it will adjust its understanding of your preferences.

This cycle of observation, learning, and action forms the basis of modern AI agents’ intelligence, enabling them to adapt to your preferences without specific programming.

How intelligent assistants improve work at Sofis Solutions

At the Sofis Solutions Solutions Division, work is underway to incorporate intelligent digital assistants (also known as “AI agents”) that help perform daily tasks more efficiently and quickly.

A practical example: the code review assistant

The Sofis Solutions code review assistant is one of the most useful collaborators in code review. Its operation is as follows:

• When a developer finishes the code for a new function or feature, the assistant reviews it automatically.
• It analyzes the code in detail, identifying possible problems, common errors, or areas for improvement.
• Additionally, it learns each developer’s patterns: recognizing both their strengths and areas where they tend to make mistakes.
• As the developer progresses on new projects, the assistant provides personalized suggestions, such as: “Be careful with this type of structure; you have had similar difficulties before.”
• If it detects that several developers have the same type of problem, the system can recommend specific training sessions on that topic.

The most valuable aspect of this assistant is its continuous learning ability. It is not just a simple review tool with predefined rules but a collaborator that adapts to each person and improves over time, like an experienced colleague who truly understands the way of working.

This is just one of several intelligent assistants Sofis Solutions uses to optimize their work, reduce errors, and support the continuous development of the team's skills.

The AI vision at Sofis Solutions

According to its vision on integrating artificial intelligence, Sofis Solutions has witnessed firsthand how intelligent assistants are transforming their way of working. It is not only about automating tasks but about incorporating digital collaborators that learn from people and alongside them.

After implementing these assistants in various internal processes and obtaining concrete results, Sofis Solutions is convinced they represent the natural next step in the evolution of enterprise software. The next generation of software will not be programmed step-by-step but orchestrated by intelligences capable of making decisions autonomously.

Sofis Solutions is leading this change, applying its experience to develop solutions where people and intelligent assistants collaborate, jointly enhancing their strengths.