Bye to the Beta Phase of AI Agents: How to Succeed in 2026

It feels like much more time has passed, but it has been just over three years since the world was introduced to generative artificial intelligence, when ChatGPT was launched in November 2022. That was only the beginning of a disruptive technology that no one wanted to miss once they saw its transformative potential for business. The result was massive and accelerated adoption — even of the next generation of the technology: AI Agents.

According to a Gartner study, in 2025, 75% of companies experimented with agents, but only 15% implemented fully autonomous systems. The vast majority used LLMs with automation for very specific and routine tasks, without fully exploiting their transformative potential.

The diagnosis was similar to GenAI, according to a report by MIT. Many companies adopted it just to avoid missing the hype, and as a result, 95% of pilots failed. So, is the technology not as disruptive as initially promised?

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Learning Gap

What happened in 2025 was the beta phase of AI Agents. The mistake was not technological but organizational. Many companies lacked the workflows to implement them properly or did not have the human capital needed to work with them. The good news for those who failed in recent months is that they have already learned from their shortcomings. But as adoption continues to be massive, it is logical that some will fall behind.

The MIT report defines this as a learning gap and highlights that any technology — no matter how powerful — requires an adaptation process when integrated into new systems. People play a key role in this task: they must get used to working with machines, but above all, they must supervise and maintain a strategic perspective. That is one of the keys to success with AI Agents. A recent study — presented as groundbreaking but only confirming what the tech sector already knew — shows that AI has no context. The challenge is to create it.

One of the most characteristic cases of poor implementation is in customer service. With poorly developed systems, customers end up frustrated and demand to be attended by a human. Their problem is not technological but a design issue. But there are also those who have already leveraged AI Agents to lead their sectors. A logistics company scaled its support operations and went from taking two hours to 90 seconds to respond to customers. Another example is a semiconductor company that developed an AI Agent capable of solving problems three times faster with a 75% success rate.

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Measurable ROI

These examples give us another key insight for this new phase of AI Agents: they must not be implemented generically but instead designed with surgical precision to resolve specific frictions. The true winners will be those who define measurable ROI through targeted implementations that add real value. Over the next 12 months, 42% of companies plan to develop AI Agents, according to a Gartner report. Incorporating them is no longer enough — the challenge now is how to implement them.

The first step is to identify high-impact solutions to eliminate frictions. Then, data must be cleaned and integrated so that the AI Agents — designed specifically for a function — can operate effectively. Once orchestration of the agentic system is achieved, along with compliance requirements, it can be scaled to new functions. This process results in a new work model built around the imperative of measurable ROI. The system must be capable of learning in order to evolve, rather than dedicating itself exclusively to fixed tasks.

This takes on a different meaning in each industry. In retail, AI Agents build intelligent ecosystems in storage centers to analyze workflows and identify employee bottlenecks; in e-commerce, they can offer customers dynamic promotions to prevent cart abandonment; in financial services, they optimize loans and fight fraud in real time. They perform all this autonomously and can be applied across all sectors.

The tools exist and are readily available. That is why the debate is no longer technological but organizational: What costs are saved, or how much does service improve when implementing it? The answer to that question holds the key to the success of AI Agents in the coming months.