AI Agents: How They Are Transforming the Workforce in 2025

AI Agents: The Frontier in Automation and Productivity

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Among the pages of Dune, there’s a recurring warning: «Thou shalt not build a machine that imitates the human mind». This is not just any commandment; it’s part of the Orange Bible Catholic, the sacred text imagined by Frank Herbert for his universe. In recent years, between the pandemic, the digital revolution, and the launch of ChatGPT, science fiction has stopped imagining a distant and supersonic future. What we are building is already here—it writes, thinks, and converses with us.
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The year of the Agents

Just a few months ago, in September 2024, Google published a technical paper about the architecture and operation of AI Agents. As 2025 begins, it’s already clear that this will be the year of the agents. According to Sam Altman, CEO of OpenAI, on his blog:

"We are now confident that we know how to build AGI as we have traditionally understood it. We believe that, in 2025, we could see the first AI agents 'joining the workforce' and materially changing company production. We continue to believe that iteratively putting great tools in people’s hands leads to excellent and broadly distributed outcomes."

And these are not just words: this week, OpenAI launched Tasks, a beta feature in ChatGPT that can autonomously execute scheduled tasks. This innovation allows for the planning and automation of future actions, from punctual reminders to recurring tasks like weekly news briefings or personalized routines. Although initially limited to Plus, Pro, and Teams users, Tasks represents OpenAI's first step toward a new generation of asynchronous AI agents.

But what is an AI agent? Or rather, how close are we to them dominating the world?

Google’s Technical Paper

Agents, The Agents technical paper, written by Julia Wiesinger, Patrick Marlow, and Vladimir Vuskovic—Google’s dream team of AI—explains the functioning of generative AI agents. What’s new? These applications extend the capabilities of language models by utilizing tools that allow them to interact with the real world. An agent is, therefore, a new layer of abstraction with a novel cognitive architecture composed of three components:

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The model

The model is the central component in the agent’s decision-making process. While it’s typically an LLM (Large Language Model), it can also consist of one or more language models of varying sizes, whether small or large. The important thing is that these models can follow reasoning based on instructions and logical frameworks, such as ReAct, Chain of Thought, or Tree of Thoughts. The model can be general-purpose, multimodal, or fine-tuned according to the agent’s specific needs. Although it is not initially trained with particular configurations, it can be optimized with examples demonstrating its capabilities in different contexts.

The tools

The tools act as a bridge between the model and the external world, enabling the agent to interact with external data and services. These can take various forms, including standard web APIs with methods like GET, POST, PATCH, and DELETE. Thanks to these tools, agents can access and process real-world information, allowing them to work with more specialized systems such as RAG (Retrieval-Augmented Generation). For Google’s models specifically, the primary types of tools they can interact with fall into three categories: Extensions, Functions, and Data Stores.

The Orchestration Layer

The orchestration layer is a cyclical process responsible for managing the agent's memory, state, reasoning, and planning. It uses prompt engineering frameworks, enabling the agent to interact more effectively with its environment and complete tasks. This cycle continues until the agent achieves its goal or reaches a predefined endpoint.

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These three components (the model, the tools, and the orchestration layer) work together to enable the agent to achieve its objectives, interact with the world, and make informed decisions. The technical paper elaborates on the architecture’s aspects and provides a comprehensive view of how these elements integrate to create effective AI agents.

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Practical Applications in the Workforce

Unlike the thinking machines of Dune, these agents should not be mysterious black boxes. Their successful implementation depends on clear and traceable processes where every decision can be monitored, the reasoning understood, and interactions documented, always focusing on security and best practices—no blind spots or gray areas.

Imagine leveraging these agents to enhance real-time data analysis, automate complex processes, and integrate multiple systems. An agent can monitor advertising campaign performance and adjust parameters automatically, connect your CRM with financial tools to generate personalized reports, or analyze customer service patterns to optimize support. The potential is especially relevant for companies managing large volumes of data or needing 24/7 operations.

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It’s Happening Now

At this moment, AI agents are a reality already transforming businesses. With frameworks like LangChain and platforms like Vertex AI or Replit, their implementation is becoming increasingly accessible and secure. The competitive advantage will belong to those who know how to adapt and implement them strategically.

Are you interested in taking the first step?
At Paisanos, we have the talent and experience to help integrate these technologies into your company responsibly and transparently. And yes, we also make sure they don’t read Dune (just in case).

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Recursos adicionales

• Introduction to Agents
• Create and Deploy Generative AI Agents Using Natural Language with Vertex AI Agent Builder

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