Overview
Artificial intelligence (AI) has recently entered a new phase, characterized by the increased deployment of so-called "AI Agents." While generative AI has already transformed how organizations function, AI Agents represent a more profound shift. They are not merely tools that produce outputs in response to prompts; rather, they are systems capable of autonomously planning, reasoning, and acting in pursuit of defined objectives. This transition from reactive to goal-oriented AI carries significant legal, regulatory, and governance implications.
Key Takeaways
- AI Agents are autonomous AI systems that can plan, decompose tasks, and execute multi-step actions across tools and environments with limited human oversight.
- Their enhanced autonomy amplifies risks that apply to AI systems generally, including security, data protection, and accountability risks.
- While AI Agents are an emerging technology, they are already subject to extensive regulation under EU law, including the EU AI Act, the General Data Protection Regulation (GDPR), the cybersecurity framework, and potentially additional regulatory regimes.
- Because this legal framework may shape the design of an AI Agent, it is essential to identify the relevant legal obligations early in the development and deployment process.
- Organizations should approach the development and deployment of AI Agents with a structured and forward-looking strategy. This includes clearly defining the purpose and intended use of the AI Agent at an early stage, carrying out a comprehensive assessment of applicable legal obligations, conducting enhanced due diligence of third-party providers and vendors involved, and establishing robust governance and oversight frameworks tailored to the specific challenges of AI Agents.
What are AI Agents?
Although no harmonized legal definition of "AI Agent" exists under EU law, the term generally refers to AI systems capable of autonomously performing sequences of actions across interconnected tools and data sources to achieve objectives, with limited human involvement. AI Agents can plan, reason, execute multi-step actions, and sequence actions in changing environments.
Their defining characteristics include task planning and decomposition; interaction with external tools and data sources (such as APIs or databases); autonomous execution of intermediate steps without requiring human approval at every step; and the ability to adapt based on feedback. In some cases, they can also modify external environments by sending communications, updating records, or initiating transactions.
Unlike traditional AI systems, which typically execute narrowly defined tasks in response to explicit instructions, AI Agents can autonomously determine how to achieve an objective, including by coordinating with other AI Agents. This distinction lies at the heart of both the opportunities and the risks.
Traditional AI systems are generally reactive and bounded, operating within predictable and predefined workflows. AI Agents, by contrast, are proactive and adaptive: they can initiate actions, interact with third-party systems, and modify their behavior over time, often with limited human intervention. This enhanced autonomy creates significant opportunities, but also raises new challenges for accountability, governance, and compliance under EU law.
What are the risks associated with AI Agents?
While AI Agents offer substantial opportunities for productivity and innovation, their enhanced autonomy also amplifies risks that apply to AI systems generally and introduces new legal, regulatory, and governance challenges.
One of the most significant risks relates to security. AI Agents often rely on extensive integrations with external tools and services, creating multiple entry points for malicious actors. Vulnerabilities such as prompt injection, data poisoning, and unauthorized access can propagate across the system, potentially enabling cascading unauthorized actions. In addition, the delegation of authority to autonomous agents raises concerns around control, as agents may access or act upon resources beyond their intended scope.
Data protection risks are equally pressing. AI Agents are designed to access, combine, and act upon data from multiple sources, which can result in extensive processing of personal data in ways that are difficult to anticipate in advance. The dynamic nature of these systems means it may be unclear what data is collected, how it is used, and for what purposes, particularly where the system autonomously determines intermediate tasks. This challenges core data protection principles, including purpose limitation, data minimization, lawfulness of processing, and transparency.
The use of persistent memory further exacerbates these concerns. By retaining contextual information across interactions, AI Agents can build detailed and evolving profiles of individuals, including by combining data from disparate sources without data subjects' awareness. This increases risks of profiling, bias amplification, and privacy breaches, while also complicating the exercise of data subject rights such as access and erasure.
Another key challenge lies in transparency and explainability. Decision-making processes in AI Agents are often distributed across multiple steps (and sometimes multiple agents), making it difficult to understand how specific outcomes are produced. This opacity may undermine accountability, reinforce automation bias, and hinder meaningful assessment of system reliability.
Finally, AI Agents raise complex questions of accountability. Where harm occurs, it may be difficult to allocate responsibility among the various actors involved, including model developers, system providers, deployers, and end users. The decentralized and adaptive nature of these systems challenges traditional liability frameworks, which are typically based on clearly defined roles and responsibilities.
How are AI Agents regulated under EU laws?
AI Agents are not subject to a dedicated regulatory framework under EU law. Instead, they are governed through a layered and overlapping set of existing legal instruments.
At the core of this framework sits the EU AI Act, which adopts a technology-neutral, risk-based approach applicable to all AI systems and General Purpose AI (GPAI) models, irrespective of their architecture. Although the EU AI Act does not define “AI Agent” as a distinct category, the European Commission has confirmed in its FAQ on the EU AI Act that AI Agents are not a separate category under the EU AI Act, but that the existing definitions of "AI system" and "GPAI model" are sufficient to bring them within scope. Accordingly, AI Agents may be subject to the obligations under the AI Act, including prohibitions of certain AI practices (Article 5), requirements for high-risk AI systems (Chapter III), and transparency obligations (Article 50). Their regulatory classification, and resulting compliance obligations, depend on the specific use case and context of deployment rather than on technical architecture alone.
However, the AI Act does not operate in isolation. AI Agents are likely to be subject to a broader stack of legal obligations under EU law. In particular, the General Data Protection Regulation (GDPR) is of central relevance wherever AI Agents process personal data. The characteristics of AI Agents, including their ability to autonomously access, combine, and act on data across multiple sources, may strain core data protection principles, such as purpose limitation, data minimization, and transparency.
The interconnected and operational nature of AI Agents may also bring them within the scope of the EU cybersecurity framework. The NIS2 Directive imposes enhanced cybersecurity risk-management and incident-reporting obligations on entities operating in essential or important sectors. To the extent that such entities deploy AI Agents, these systems may need to be incorporated into their overall cybersecurity risk posture. In parallel, the Cyber Resilience Act introduces horizontal security requirements for products with digital elements. Where AI Agents are embedded within or form part of such products, they may need to comply with secure-by-design obligations, vulnerability management processes, and lifecycle security requirements.
Beyond data protection and cybersecurity, additional regulatory regimes may apply depending on the deployment context. Where AI Agents interact directly with users or consumers, EU consumer protection laws may become relevant. Where AI Agents are deployed in the context of intermediary services, such as online platforms, the Digital Services Act could be applicable. Sector-specific regulations may also apply — for instance, financial entities deploying AI Agents may need to account for the operational resilience and third-party risk requirements under the Digital Operational Resilience Act (DORA). Finally, the use of AI Agents may also engage EU liability frameworks, including the Product Liability Directive, particularly where autonomous actions result in damage.
In practice, while AI Agents are an emerging technology, they are already subject to extensive regulation under EU law. Given that this legal framework may shape the design of an AI Agent, it is essential to identify the relevant legal obligations early in the development and deployment processes.
What should organizations do prior to deploying AI Agents?
Against this backdrop, organizations should approach the development and deployment of AI Agents with a structured and forward-looking strategy. Given the breadth of applicable EU requirements and the extent to which compliance considerations may shape system design, preparation is critical.
First, organizations should clearly define the purpose and intended use of the AI Agent at an early stage. Under the EU regulatory framework, compliance obligations hinge on the system's intended purpose and reasonably foreseeable uses. A vague or overly broad definition may lead to underestimating regulatory exposure or, conversely, unnecessarily constraining the system's deployment.
Second, organizations should carry out a comprehensive assessment of applicable legal obligations across the relevant frameworks. This includes not only determining whether the AI Agent falls within the scope of the EU AI Act and its risk classification, but also identifying parallel obligations under the GDPR, cybersecurity legislation, consumer protection rules, and any sector-specific requirements. Importantly, this assessment should be dynamic, taking into account how the system may evolve or be used over time.
Third, enhanced due diligence of third-party providers and vendors becomes essential. AI Agents are rarely developed or deployed in isolation; they typically rely on a complex ecosystem of models, tools, APIs, and infrastructure providers. Organizations must therefore ensure that their contractual arrangements, technical integrations, and data flows are aligned with applicable legal requirements. This includes verifying providers' compliance posture, understanding their role within the regulatory value chain, and assessing potential risks.
Finally, organizations should establish robust governance and oversight frameworks tailored to the specific challenges of AI Agents. This goes beyond traditional AI governance structures. It requires clear accountability for system design, deployment, and monitoring, as well as mechanisms to control autonomy, track system behavior, and intervene where necessary. In particular, governance frameworks should ensure traceability of actions, enable meaningful human oversight where appropriate, and support continuous monitoring and risk reassessment throughout the system's lifecycle.
Organizations that anticipate and integrate the risks and regulatory requirements related to AI Agents from the outset will be better positioned not only to ensure compliance, but also to unlock the potential of AI Agents in a sustainable and legally sound manner.
