Driving Compliance with the EU’s AI Act through Agentic AI Agents
The EU’s AI Act sends a clear signal to organisations: risk governance, transparency, and accountability are no longer optional; they are central to being compliant. While much attention currently goes to the opportunities offered by Generative AI, forward-looking organisations can leapfrog their AI compliance using Agentic AI.
Understanding Agentic AI
Interpretation of the AI Act is often grounded in traditional Generative AI use cases. However, the emergence of Agentic AI—which learns, acts, and adapts autonomously—promises to reshape compliance strategies. Unlike traditional monolithic generative models, agentic AI operates with a high degree of autonomy, allowing it to:
- Pursue goals rather than merely producing outputs.
- Learn and adapt dynamically, updating strategies or behaviours over time.
- Take action across both digital and physical systems.
These systems integrate core problem-solving capabilities, including memory, planning, orchestration, and the ability to interact with external applications, making them highly effective in optimising processes and executing decisions autonomously.
A New Risk Landscape
As Agentic AI systems become more autonomous, they fundamentally shift the risk profile. This evolution introduces several new challenges:
- Emergent behavior: As agents learn through interaction, their behaviour can shift in unexpected ways, making static risk assessments insufficient.
- External integration risk: Agentic systems often autonomously interface with third-party tools, expanding the attack surface and creating complex security environments.
- The accountability gap: With countless micro-decisions, tracing why something happened becomes difficult, complicating compliance with transparency and auditability standards.
Interpreting the AI Act through an Agentic Lens
While the AI Act provides a strong foundation, applying its requirements to Agentic AI necessitates a reinterpretation in four key areas:
1. Continuous Risk Management
Risk management must account for real-time evolution and be ecosystem-aware. The current framework places primary responsibility on providers, while users must notify providers of emerging risks. Compliance must ensure consistent reliability in dynamic environments and account for failure modes over time.
2. Dynamic Human Oversight
Manual approvals are too slow; oversight must be embedded within the system through dynamic guardrails and real-time intervention points. Both providers and deployers share responsibility for effective oversight.
3. Evolving Transparency
Transparency must reflect the system’s evolution and complexity. Ongoing insights into system behaviour are essential, requiring user-friendly explanations of complex decision-making processes.
4. Dynamic and Auditable Documentation
Agentic AI necessitates living documentation that is regularly updated. Retaining interpretable and relevant data is crucial for supporting audits and investigations.
From Principle to Practice: Governing Agentic AI
Identifying risks is merely the beginning; translating the AI Act’s high-level requirements into operational governance is the real challenge. Here are three practical priorities:
- Shared, ongoing risk assessment: Providers must create tools for detecting emergent risks, while deployers must monitor real-world system behaviour.
- Dynamic transparency and real-time monitoring: Agentic AI systems require traceability infrastructure, including unique system IDs and activity logs that explain decision-making processes.
- Adaptive oversight: Controls must scale with speed, incorporating automated safeguards and AI-literate human operators who can intervene when necessary.
Conclusion
The core pillars of the AI Act—risk management, transparency, and oversight—remain relevant, but the application must evolve. Agentic AI governance requires a continuous, interpretative, and collaborative approach. Governing these systems is not just a technical task; it’s a shared responsibility and an opportunity for organisations to lead in building intelligent, safe, accountable, and trustworthy AI systems.
