The age of AI Agents: when intelligence stops responding and starts deciding

The conversation about artificial intelligence has grown exponentially in the last two years. However, behind the enthusiasm, there is a less debated reality. An internal study we conducted brings the data that although more than 70% of digital interactions with customers already involve some level of automation, less than 15% generate direct impact on revenue, operational efficiency or relevant business decisions. The reason is simple and structural: automating is not the same as deciding. 

For years, the focus has been on accelerating tasks, reducing friction and scaling operations.First with rules, then with bots, and then with AI applied to isolated processes.This evolution was necessary, but exposed a clear limit. Companies execute faster than ever, but continue to make critical decisions in a late, fragmented and dependent on human interpretation under pressure.The execution was automated. 

When entering 2026, the question is no longer whether AI should be used, but where it needs to be to improve the quality of decisions. Real business operates in unpredictable environments, with customers changing their minds, mixing subjects, returning days later and expecting continuity. Decisions do not depend only on the question asked, but on the history, the moment, the channel and the objective of interaction. In this context, casted systems, based on fixed flows and predefined answers, cease to scale. Not because of technical failure, but because they were designed for a world where answering correctly was enough. 

The real leap in AI came not from a single innovation, but from the convergence of concrete advances: more capable models, better understanding of context, and the ability to maintain memory, goals, and states over time. AI has moved from being purely reactive to operating more autonomously. It is no longer limited to answering isolated questions. It can interpret complete conversations, recognize patterns, connect signals from multiple sources, and make decisions based on intention, not just keywords. 

This is where AI Agents arise. An AI Agent does not operate from scripts, but from objectives. It understands the context of the conversation, considers previous interactions, maintains a clear business objective and decides what is the next most appropriate step.In addition, it performs real actions within the company's systems and learns from the result of each interaction. AI is no longer just an interface and becomes a decision system in production. 

This change is relevant because the most impactful decisions in business do not happen in committees or dashboards. They occur daily, millions of times, on the front line of the operation. Decide what to say to a specific customer, what to offer at that moment, when to insist, when to wait, when to climb. These are decisions that seem small in appearance, but are giant in impact when repeated at scale. This type of decision lives in conversations, weak signals, changes in tone, hesitations, subtle deviations in behavior, and in accumulated context. It does not work with fixed rules. 

It is precisely in this territory that AI Agents cease to be a promise and become inevitable. They do not execute instructions. They exercise operational criteria. A criterion that previously depended exclusively on people, individual experience and human judgment, and that can now be designed, trained, governed and replicated within systems. 

At Yalo, this approach has been built over more than a decade, from the continuous operation of millions of conversations and business decisions in different contexts, sales, payments, credit, billing, retention and service, distributed among channels such as WhatsApp, voice calls, applications and web. This experience has shown, in practice, that decisions at scale are not solved with scripts or rigid automations, but need to happen at the time of interaction, combining historical context, transactional data, business rules and continuous learning. From this, conversational agents have come to be treated not only as interfaces, but as decision operational units within systems. 

Looking to 2026 is not making predictions. It is naming a change already underway. Organizations that understand the Agentic Era they will design structures capable of deciding better, faster and with consistency. Those that do not understand will continue surrounded by automation, performing tasks at scale, but stuck to the same decision bottleneck: fixed rules, lack of context and constant dependence on human intervention. This transition requires clarity, because what is at stake is not adding more AI, but overcoming the model in which technology performs, but does not decide. Automating was the first step. Deciding, with agents, will be the competitive advantage. 

*By Andres Stella, COO of Yalo.