AI is no longer just responsive, now it is also acting

The difference is substantial. While traditional generative models are limited to producing responses or recommendations, AI agents can execute actions, interact with corporate systems, and complete tasks autonomously or semi-autonomously.

In other words, AI is no longer just responsive. Now it is also acting.

From the digital assistant to the operational agent

An AI agent can interpret a request received via email, query corporate applications, access databases, generate documentation, initiate approval processes, update records, or coordinate multiple tools to achieve a given goal.

This capability opens up significant opportunities for organizations. Agents can intervene in areas as diverse as:

• Customer service.
• Finance and accounting.
• Purchasing and supplier management.
• Human resources.
• Legal services and compliance.
• Operations.
• Technology and cybersecurity.

The potential result goes beyond a one-off improvement in productivity. It allows you to automate entire processes, reduce execution times, improve operational capacity and free up resources for higher value-added activities.

More range, more value and also more risk

Autonomy is precisely the element that makes AI agents attractive. However, it is also the characteristic that requires more rigorous risk management.

When a system has the capacity to act, an incorrect decision ceases to be a mere informational error to become an action with possible real consequences.

An agent could, for example:

• Processing a financial transaction incorrectly.
• Improperly modifying corporate records.
• Sharing sensitive information with third parties.
• Execute unauthorized actions on critical systems.
• Generate regulatory or contractual breaches.

The challenge is no longer just to verify whether an answer is correct, but to ensure that the actions executed by the agent are secure, traceable and in accordance with the limits defined by the organization.

When risks are no longer theoretical

The risks associated with AI agents are no longer a hypothesis.

In 2026, during advanced cybersecurity tests, several experimental models managed to circumvent technical restrictions designed to isolate them and executed unforeseen actions that affected third-party systems, in the so-called  Hugging Face incident. This event highlighted the need to strengthen supervision, containment and control mechanisms on systems with high levels of autonomy.

At the same time, the Spanish Data Protection Agency has recently reported on the first notification of a personal data breach resulting from an attack executed by an AI agent, showing that this type of risk is already part of the real threat scenario faced by organizations.

All this reinforces a key conclusion: trust in AI is not about assuming that it will always act correctly, but about designing mechanisms that allow errors to be prevented, detected, limited and corrected when they occur.

Governance as a differentiating factor

Faced with this new scenario, the question is no longer whether organizations should adopt AI agents. The real question is under what conditions they should delegate decisions and actions to these systems.

The answer lies in implementing a solid governance model that balances innovation, efficiency and control. Organizations must define, among other aspects:

• What tasks can be automated.
• What level of autonomy is acceptable in each process.
• What decisions require human supervision.
• How the traceability of the actions carried out is guaranteed.
• What measures should be implemented to avoid misuse or undesired results.
• How incidents are handled and associated liabilities.

A regulatory and business challenge

The implementation of AI agents requires requirements from different regulatory areas to be considered together.

The European Union's AI Regulation (RIA), the data protection regulation (GDPR) and cybersecurity regulation (NIS2, Cyber Resilience Regulation), industry requirements and internal control policies must converge in a single risk management model. It is not a question of creating independent layers of compliance, but of developing an integrated governance framework that allows demonstrating that systems are secure, monitorable and aligned with business objectives.

Organizations that succeed in doing so will be better able to capture the value of this new generation of technologies while maintaining the trust of customers, employees, partners, and regulators.

The race is no longer for having AI, but for governing it

AI agents represent one of the most relevant technological changes since the arrival of generative AI.

Their ability to execute actions and operate on business processes can radically transform the way organizations work. However, as autonomy increases, so does the need for control.

The competitive advantage will not only come from implementing AI agents, but from doing so with adequate governance, effective supervision and risk management capable of turning trust into a strategic asset.