Why the EU AI Act Matters for Agentic AI
Artificial intelligence is evolving rapidly. We’re moving from prompt-based systems to more autonomous, goal-driven technologies known as agentic AI. These systems can take independent actions, collaborate with other agents, and interact with external systems—all with limited human input. This shift introduces serious questions about governance, oversight, and security.
The EU Artificial Intelligence Act (EU AI Act) is the first major regulatory framework to address AI safety and compliance at scale. Based on a risk-based classification model, it sets clear, enforceable obligations for how AI systems are built, deployed, and managed. In addition to the core legislation, the European Commission will release a voluntary AI Code of Practice by mid-2025 to support industry readiness.
As agentic AI becomes more common in real-world systems, organizations must prepare now. These systems often fall into regulatory gray areas due to their autonomy, evolving behavior, and ability to operate across environments. Companies using or developing agentic AI need to evaluate how these technologies align with EU AI Act requirements—and whether additional internal safeguards are needed to remain compliant and secure.
This blog outlines how the EU AI Act may apply to agentic AI systems, where regulatory gaps exist, and how organizations can strengthen oversight and mitigate risk using purpose-built solutions like HiddenLayer.
What Is Agentic AI?
Agentic AI refers to systems that can autonomously perform tasks, make decisions, design workflows, and interact with tools or other agents to accomplish goals. While human users typically set objectives, the system independently determines how to achieve them. These systems differ from traditional generative AI, which typically responds to inputs without initiative, in that they actively execute complex plans.
Key Capabilities of Agentic AI:
- Autonomy: Operates with minimal supervision by making decisions and executing tasks across environments.
- Reasoning: Uses internal logic and structured planning to meet objectives, rather than relying solely on prompt-response behavior.
- Resource Orchestration: Calls external tools or APIs to complete steps in a task or retrieve data.
- Multi-Agent Collaboration: Delegates tasks or coordinates with other agents to solve problems.
- Contextual Memory: Retains past interactions and adapts based on new data or feedback.
IBM reports that 62% of supply chain leaders already see agentic AI as a critical accelerator for operational speed. However, this speed comes with complexity, and that requires stronger oversight, transparency, and risk management.
For a deeper technical breakdown of these systems, see our blog: Securing Agentic AI: A Beginner’s Guide.
Where the EU AI Act Falls Short on Agentic Systems
Agentic systems offer clear business value, but their unique behaviors pose challenges for existing regulatory frameworks. Below are six areas where the EU AI Act may need reinterpretation or expansion to adequately cover agentic AI.
1. Lack of Definition
The EU AI Act doesn’t explicitly define “agentic systems.” While its language covers autonomous and adaptive AI, the absence of a direct reference creates uncertainty. Recital 12 acknowledges that AI can operate independently, but further clarification is needed to determine how agentic systems fit within this definition, and what obligations apply.
2. Risk Classification Limitations
The Act assigns AI systems to four risk levels: unacceptable, high, limited, and minimal. But agentic AI may introduce context-dependent or emergent risks not captured by current models. Risk assessment should go beyond intended use and include a system’s level of autonomy, the complexity of its decision-making, and the industry in which it operates.
3. Human Oversight Requirements
The Act mandates meaningful human oversight for high-risk systems. Agentic AI complicates this: these systems are designed to reduce human involvement. Rather than eliminating oversight, this highlights the need to redefine oversight for autonomy. Organizations should develop adaptive controls, such as approval thresholds or guardrails, based on the risk level and system behavior.
4. Technical Documentation Gaps
While Article 11 of the EU AI Act requires detailed technical documentation for high-risk AI systems, agentic AI demands a more comprehensive level of transparency. Traditional documentation practices such as model cards or AI Bills of Materials (AIBOMs) must be extended to include:
- Decision pathways
- Tool usage logic
- Agent-to-agent communication
- External tool access protocols
This depth is essential for auditing and compliance, especially when systems behave dynamically or interact with third-party APIs.
5. Risk Management System Complexity
Article 9 mandates that high-risk AI systems include a documented risk management process. For agentic AI, this must go beyond one-time validation to include ongoing testing, real-time monitoring, and clearly defined response strategies. Because these systems engage in multi-step decision-making and operate autonomously, they require continuous safeguards, escalation protocols, and oversight mechanisms to manage the emergent and evolving risks they pose throughout their lifecycle.
6. Record-Keeping for Autonomous Behavior
Agentic systems make independent decisions and generate logs across environments. Article 12 requires event recording throughout the AI lifecycle. Structured logs, including timestamps, reasoning chains, and tool usage, are critical for post-incident analysis, compliance, and accountability.
The Cost of Non-Compliance
The EU AI Act imposes steep penalties for non-compliance:
- Up to €35 million or 7% of global annual turnover for prohibited practices
- Up to €15 million or 3% for violations involving high-risk AI systems
- Up to €7.5 million or 1% for providing false information
These fines are only part of the equation. Reputational damage, loss of customer trust, and operational disruption often cost more than the fine itself. Proactive compliance builds trust and reduces long-term risk.
Unique Security Threats Facing Agentic AI
Agentic systems aren’t just regulatory challenges. They also introduce new attack surfaces. These include:
- Prompt Injection: Malicious input embedded in external data sources manipulates agent behavior.
- PII Leakage: Unintentional exposure of sensitive data while completing tasks.
- Model Tampering: Inputs crafted to influence or mislead the agent’s decisions.
- Data Poisoning: Compromised feedback loops degrade agent performance.
- Model Extraction: Repeated querying reveals model logic or proprietary processes.
These threats jeopardize operational integrity and compliance with the EU AI Act’s demands for transparency, security, and oversight.
How HiddenLayer Supports Agentic AI Security and Compliance
At HiddenLayer, we’ve developed solutions designed specifically to secure and govern agentic systems. Our AI Detection and Response (AIDR) platform addresses the unique risks and compliance challenges posed by autonomous agents.
Human Oversight
AIDR enables real-time visibility into agent behavior, intent, and tool use. It supports guardrails, approval thresholds, and deviation alerts, making human oversight possible even in autonomous systems.
Technical Documentation
AIDR automatically logs agent activities, tool usage, decision flows, and escalation triggers. These logs support Article 11 requirements and improve system transparency.
Risk Management
AIDR conducts continuous risk assessment and behavioral monitoring. It enables:
- Anomaly detection during task execution
- Sensitive data protection enforcement
- Prompt injection defense
These controls support Article 9’s requirement for risk management across the AI system lifecycle.
Record-Keeping
AIDR structures and stores audit-ready logs to support Article 12 compliance. This ensures teams can trace system actions and demonstrate accountability.
By implementing AIDR, organizations reduce the risk of non-compliance, improve incident response, and demonstrate leadership in secure AI deployment.
What Enterprises Should Do Next
Even if the EU AI Act doesn’t yet call out agentic systems by name, that time is coming. Enterprises should take proactive steps now:
- Assess Your Risk Profile: Understand where and how agentic AI fits into your organization’s operations and threat landscape.
- Develop a Scalable AI Strategy: Align deployment plans with your business goals and risk appetite.
- Build Cross-Functional Governance: Involve legal, compliance, security, and engineering teams in oversight.
- Invest in Internal Education: Ensure teams understand agentic AI, how it operates, and what risks it introduces.
- Operationalize Oversight: Adopt tools and practices that enable continuous monitoring, incident detection, and lifecycle management.
Being early to address these issues is not just about compliance. It’s about building a secure, resilient foundation for AI adoption.
Conclusion
As AI systems become more autonomous and integrated into core business processes, they present both opportunity and risk. The EU AI Act offers a structured framework for governance, but its effectiveness depends on how organizations prepare.
Agentic AI systems will test the boundaries of existing regulation. Enterprises that adopt proactive governance strategies and implement platforms like HiddenLayer’s AIDR can ensure compliance, reduce risk, and protect the trust of their stakeholders.
Now is the time to act. Compliance isn’t a checkbox, it’s a competitive advantage in the age of autonomous AI.
Have questions about how to secure your agentic systems? Talk to a HiddenLayer team member today: contact us.
