Artificial intelligence is entering a new stage of development marked by the emergence of systems capable not only of answering questions or analyzing information, but also of making decisions and executing actions autonomously. This evolution, known as agentic artificial intelligence, is generating an intense debate among governments, technology companies, and regulatory bodies due to the impact it could have on strategic sectors such as finance. Unlike traditional models, these new systems can establish objectives, plan tasks, use digital tools, and modify their actions according to the results obtained.
The advancement of agentic artificial intelligence has awakened growing concern among financial authorities because current regulatory frameworks were designed mainly to supervise decisions made by people or by systems with limited functions. The possibility that autonomous agents operate within banks, investment funds, or payment platforms raises complex questions about responsibility, supervision, and economic stability. Sarah Breeden, Deputy Governor of the Bank of England responsible for financial stability, pointed out that this new generation of artificial intelligence could require regulatory reforms because current rules do not fully contemplate systems capable of acting with high levels of autonomy.
The debate does not focus solely on risks, but also on the enormous opportunities offered by this technology. Artificial intelligence can help financial institutions detect fraud, analyze large volumes of data, improve risk management, and offer more efficient services to customers. However, the greater the decision-making capacity of these systems, the greater the need to create mechanisms that guarantee that their actions remain under control and do not generate unexpected consequences for the economy.
From Traditional Artificial Intelligence to Agentic Artificial Intelligence
During recent years, artificial intelligence has been used mainly as an assistance tool capable of analyzing information, automating processes, and supporting decision-making. Machine learning systems have made it possible to analyze financial data, detect behavioral patterns, predict economic trends, and improve different administrative processes. Although these technologies have transformed numerous sectors, they generally operated within established limits and depended on a person or an organization to make final decisions, an important difference compared to the current evolution of agentic artificial intelligence.
Agentic artificial intelligence introduces a fundamental difference because it incorporates capabilities related to autonomy and allows systems to develop more complex actions. Through agentic artificial intelligence, an artificial intelligence agent can receive a general objective, divide it into different tasks, select methods to achieve that objective, and execute actions without each step having to be approved by a human user. This characteristic makes agentic artificial intelligence a much more advanced technology, although it also increases the complexity of its supervision and control.
In the financial sector, the expansion of agentic artificial intelligence could mean that an artificial intelligence agent is capable of analyzing international markets, assessing risks, modifying investment strategies, or managing commercial operations automatically. The application of agentic artificial intelligence in these areas could improve the speed and efficiency of financial institutions, as these systems can process large amounts of information and act quickly. However, it could also generate new problems if agentic artificial intelligence makes incorrect decisions, misinterprets an economic situation, or acts differently from what its designers expected.
The main difference between a traditional algorithm and a system based on agentic artificial intelligence is that the latter not only processes information but also actively participates in the planning and execution of decisions. This transition toward agentic artificial intelligence requires a rethinking of basic concepts of financial regulation, since many current rules depend on identifying who made a decision and under what criteria it was made. When an action arises from a chain of decisions executed by an autonomous artificial intelligence, as occurs in the most advanced models of agentic artificial intelligence, determining responsibilities can become much more complicated.
The impact of autonomous artificial intelligence on the financial system
The financial system is one of the sectors where agentic artificial intelligence could have the greatest effects due to the speed at which operations are carried out and the enormous amount of data used daily. Banks, insurance companies, investment funds, and stock markets already use advanced algorithms to improve their internal processes, but the arrival of agentic artificial intelligence could significantly expand these capabilities by allowing systems not only to analyze information but also to directly act on the data they process. This evolution represents a significant change in the way financial institutions use automation.
Financial markets operate through a complex network of connections between institutions, investors, and technological platforms. A decision made by one participant can quickly affect other actors, especially when there is economic uncertainty or crisis situations. For this reason, regulators closely monitor the progress of agentic artificial intelligence, since a massive adoption of autonomous agents with similar behaviors could generate coordinated responses that increase market volatility.
A possible scenario would be that different institutions use systems based on agentic artificial intelligence trained with similar data and designed to achieve similar objectives. In the face of an unexpected economic event, several autonomous agents could interpret the situation in a similar way and execute related operations at the same time. Although each agentic artificial intelligence system would act independently, the combined result could produce financial movements that are difficult to control due to the speed and scale of their decisions.
This concern does not mean that agentic artificial intelligence will necessarily cause a financial crisis. Regulators recognize that this technology can provide significant benefits to the sector, especially in areas such as early risk detection, fraud prevention, and process optimization. The challenge is to develop rules capable of taking advantage of the benefits of agentic artificial intelligence without allowing the risks associated with its autonomy to grow without adequate supervision.
The challenge of maintaining human oversight over autonomous systems
One of the fundamental principles in artificial intelligence regulation has been the need to maintain human oversight over important decisions. The traditional idea is that a person should have the ability to review, modify, or stop actions carried out by an automated system. However, this strategy may be difficult to apply when systems based on agentic artificial intelligence operate at speeds beyond human analytical capacity and can execute multiple actions autonomously.
Sarah Breeden has pointed out that the idea of always having a human being involved in every decision may not be sufficient for the most advanced agentic artificial intelligence systems. In certain financial environments, operational speed may make immediate human review impossible before an action has consequences. Therefore, regulators consider it necessary to develop new forms of control adapted to a more autonomous technology, especially when artificial intelligence agents can make complex decisions without constant intervention.
One alternative would be to create automated supervision systems capable of monitoring the behavior of other agentic artificial intelligence systems. These mechanisms could identify unusual operations, establish action limits, and stop processes when they detect behaviors considered dangerous. In this way, supervision would no longer depend solely on a person and would instead combine human controls with technological tools specifically designed to manage the risks associated with the autonomy of agentic artificial intelligence.
The issue of responsibility continues to be one of the main challenges related to the expansion of agentic artificial intelligence. If an autonomous agent causes significant economic losses, there are several possibilities regarding who should be responsible for those consequences. Responsibility could fall on the financial institution that used the system, the technology company that developed the model, or those responsible for establishing the operating parameters of this technology.
The need for new regulation for financial artificial intelligence
The expansion of autonomous agents based on agentic artificial intelligence is encouraging regulators to study new forms of financial supervision. The objective is not to stop technological innovation, but to ensure that institutions use these tools safely and responsibly. Future regulations will likely need to focus less on specific technologies and more on the capabilities possessed by agentic artificial intelligence systems and the risks they may generate when they act autonomously.
One of the most important changes could be related to the transparency of agentic artificial intelligence models used by financial institutions. Regulatory bodies need to understand how these systems work and what factors influence the decisions they make. Although advanced artificial intelligence models can be extremely complex, it will be necessary to establish mechanisms that allow their results to be analyzed, their processes to be evaluated, and possible errors to be detected before they generate economic consequences.
It will also be essential to improve security testing before implementing agentic artificial intelligence systems in financial environments. Just as banks conduct stress tests to evaluate their ability to withstand economic crises, specific assessments may be required to determine how autonomous agents react to unexpected situations. These tests would make it possible to identify vulnerabilities related to automated decision-making before they affect the real financial system.
Another relevant aspect will be establishing operational control mechanisms for agentic artificial intelligence. Institutions may need systems capable of automatically limiting an agent’s actions when it exceeds certain risk levels or when its behavior moves away from established objectives. The ability to stop, modify, or restrict the operation of an autonomous system will be an essential feature for maintaining financial stability in an environment where decisions can occur within seconds.
Artificial intelligence and the increase in cybersecurity risks
Cybersecurity has become one of the main concerns related to the expansion of agentic artificial intelligence and other autonomous systems. Financial institutions are already frequent targets of digital attacks due to the value of the information they manage and the economic impact their operations can generate. The arrival of intelligent agents adds a new dimension to the problem because these systems can become both defensive tools and potential weak points within financial infrastructure.
Banks could use advanced agentic artificial intelligence to detect threats, analyze suspicious behaviors, and respond quickly to cyberattacks. The ability of these systems to process enormous amounts of data would allow them to identify patterns that are difficult to detect through traditional methods. The application of agentic artificial intelligence in cybersecurity could significantly strengthen financial system protection by enabling faster and more accurate responses.
However, the same technology could be used by attackers to develop more sophisticated strategies. A poorly configured agentic artificial intelligence system, one that has been manipulated, or one trained with incorrect information could execute harmful actions before human teams identify the problem. Additionally, attackers could attempt to alter the data used by these models to modify their decisions and exploit the autonomy of the agents.
For this reason, the regulation of financial agentic artificial intelligence will need to include specific cybersecurity measures. It will not be enough to control how systems make decisions; it will also be necessary to protect them against external manipulation and ensure that their processes are secure. Technological protection will become a fundamental element of economic stability at a stage where artificial intelligence will have an increasingly important role in finance.
Technological innovation versus regulatory caution
The development of agentic artificial intelligence creates a complex balance between encouraging innovation and avoiding unnecessary risks. Financial authorities do not want to prevent companies from taking advantage of a technology that can improve sector efficiency, optimize processes, and transform the way decisions are made. At the same time, there is concern that excessively rapid adoption of agentic artificial intelligence could generate problems that would be difficult to solve later if autonomous systems reach a level of influence greater than expected.
Economic history shows that new technologies can transform markets positively, but they can also create risks when they are used without adequate controls. The 2008 financial crisis demonstrated how complex and poorly understood systems can contribute to the accumulation of vulnerabilities within the economic system. Although agentic artificial intelligence represents a different challenge, the need to understand, monitor, and establish limits for new technological tools remains fundamental.
Regulators seek to prevent a situation in which the evolution of agentic artificial intelligence advances faster than institutional capacity to control it. Creating adaptable regulations will be essential because artificial intelligence systems constantly evolve and rapidly acquire new capabilities. Overly rigid regulation could become obsolete quickly, while excessively weak regulation could allow the risks associated with the autonomy of agentic artificial intelligence to reach a systemic scale.
The future will likely not be defined by a complete replacement of human work, but by closer collaboration between people and intelligent systems. Agentic artificial intelligence can become a complementary tool to improve productivity and decision-making, provided that adequate supervision exists. The key will be ensuring that important decisions remain aligned with economic and social objectives defined by humans.
A transformation that will affect the entire global economy
Although the financial sector currently occupies much of the debate, agentic artificial intelligence will have much broader effects on the global economy. Autonomous agents could be incorporated into areas such as commerce, logistics, scientific research, healthcare, public administration, and business management. In all these sectors, similar questions will arise regarding the control, responsibility, transparency, and security of systems based on agentic artificial intelligence.
The difference with the financial system is that its effects can quickly spread throughout the rest of the economy. An incorrect decision made by a company’s internal system may have limited consequences, but a chain of errors in interconnected financial markets can affect millions of people. For this reason, central banks are trying to anticipate the growth of agentic artificial intelligence and understand how its use may influence economic stability.
The regulation of artificial intelligence, especially systems with autonomous capabilities, will be one of the major economic challenges of the coming years. Governments will need to collaborate with technology companies, financial institutions, and independent experts to develop effective supervision models that allow the benefits of agentic artificial intelligence to be leveraged without increasing risks. International cooperation will be particularly important because digital markets operate beyond national borders and require common criteria for action.
Agentic artificial intelligence is changing the way institutions understand automation, process management, and decision-making. It is no longer only about using machines to perform calculations, analyze information, or execute repetitive tasks, but about managing systems capable of acting with a certain degree of independence and adapting to different scenarios. This change driven by agentic artificial intelligence requires financial regulators to review their traditional tools and adapt them to a new technological reality in which autonomous systems will have an increasingly significant presence.
The warnings from the Bank of England reflect a concern shared by many international authorities: systems based on agentic artificial intelligence can offer major opportunities to improve efficiency, reduce risks, and optimize processes, but they require rules specifically designed for their characteristics. Transparency, accountability, and security will be essential elements to ensure that agentic artificial intelligence contributes to economic growth without becoming a source of instability for financial markets and other strategic sectors.
The future of finance will be marked by an increasing combination of human intelligence and artificial intelligence. Agentic artificial intelligence should not be understood solely as a replacement for traditional processes, but as a new tool capable of expanding organizational capabilities when implemented with an appropriate strategy. The goal of regulation will not be to stop this technological evolution, but to create the necessary conditions for artificial intelligence to advance in a safe, responsible, and beneficial way for society.
In this new scenario, having the support of digital transformation specialists will be key to implementing solutions based on agentic artificial intelligence efficiently and securely. ITD Consulting helps organizations develop technology strategies, optimize their digital processes, and address the challenges arising from the incorporation of new artificial intelligence tools. To learn how to apply these technologies in your company and design solutions adapted to your needs, you can contact the ITD Consulting team by writing to [email protected].
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