How to use agentic AI in line with the EU AI Act

The EU AI Act provides a structured framework to regulate AI applications based on risk – and that includes agentic AI. Discover the impact on everything from data protection to copilots

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As agentic AI systems – those capable of autonomous decision-making and independent goal-setting – gain prominence in business applications, customer experience (CX) professionals must navigate the European Union’s AI Act to ensure compliance.

Unlike traditional robotic process automation (RPA), which follows predefined rules, agentic AI operates dynamically, adapting to new data and situations without direct human oversight. This shift introduces both opportunities and regulatory challenges.

The EU AI Act does not specifically name agentic AI, but it does provide a structured framework to regulate all AI applications based on their potential risk levels, ensuring that agentic AI is deployed ethically, transparently and accountably.

This article explores the key differences between agentic AI and RPA, the specific provisions of the EU AI Act governing agentic AI and what customer-facing departments must consider when integrating these advanced tools.

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Agentic AI versus RPA

Understanding the difference between agentic AI and RPA is crucial for compliance and implementation. RPA automates repetitive tasks with rule-based execution, requiring explicit programming, whereas agentic AI exhibits autonomy, setting and pursuing goals dynamically based on real-time data and contextual learning.

While RPA follows static workflows and predefined triggers, agentic AI continuously evolves, improving through reinforcement learning and complex decision modeling. These differences mean that RPA presents minimal regulatory concerns as it executes deterministic operations, whereas agentic AI, due to its independent decision-making, falls under stricter governance, requiring risk classification and oversight under the EU AI Act.

The EU AI Act categorizes AI applications based on their societal impact and assigns regulatory requirements accordingly.

Given its ability to make autonomous decisions, agentic AI often falls under high-risk AI systems, especially when used in customer interactions, financial services or decision-making affecting fundamental rights.

Transparency and explainability are key requirements, as the Act mandates customers must be clearly informed when interacting with an AI system rather than a human. AI-driven decisions that affect customer options, such as dynamic pricing or credit assessments, must include explanations that are easily understood.

Protecting your data

Data protection and privacy are also emphasized in the Act. Agentic AI must comply with strict data minimization principles, ensuring that it only collects necessary data. Customers should have control over their data, with options to view, modify, or delete information collected by AI systems.

High-risk agentic AI tools require rigorous testing, documentation and external review before deployment. Companies must assess AI models for biases, particularly in applications influencing hiring, finance or personalized recommendations.

Governance and oversight are essential, requiring businesses to establish governance structures to monitor AI compliance and respond to potential risks. Assigning an AI compliance officer or forming a governance committee ensures accountability and swift resolution of ethical concerns.

For customer-facing departments, the integration of agentic AI has significant implications.

Transparency in AI interactions and accessible explanations for AI-driven decisions are key to maintaining customer trust. Creating dedicated support channels for AI-related concerns ensures that customers have human recourse if needed.

Implementing hybrid models where AI augments human customer service rather than fully replacing it can strike a balance between efficiency and trust.

Human oversight should be maintained for AI decisions that significantly impact customer outcomes, such as eligibility for services. Continuous monitoring and auditing are critical, requiring regular assessments of AI performance for fairness, accuracy and unintended biases, while also maintaining documentation to comply with regulatory inquiries and audits.

Flexibility and customer control

Flexibility and customer control are also important considerations. Allowing customers to opt in or out of AI-driven personalization features and providing easy-to-use interfaces for managing AI interactions and data preferences contribute to a more transparent and user-friendly experience.

To stay ahead of regulatory demands, CX leaders should foster AI literacy within their teams, develop transparent policies and collaborate with legal and technical experts. Investing in compliance-ready AI solutions, bias-detection mechanisms and explainability tools will ensure ethical and effective AI integration.

Agentic AI represents a transformative shift in CX, allowing businesses to offer highly personalized, efficient and scalable services. However, it also raises fundamental questions about responsibility and accountability.

By proactively aligning AI-driven customer interactions with the EU AI Act’s principles of fairness, transparency and accountability, businesses can build trust while harnessing the full potential of agentic AI. This requires a commitment to ongoing learning and adaptation as AI technologies and regulatory landscapes continue to evolve.

Automating decision-making

Another critical area where agentic AI impacts CX is the automation of complex decision-making processes. AI-powered chatbots and virtual assistants can now resolve issues, recommend products and process transactions autonomously. However, the challenge arises when AI decisions are contested or when customers require human assistance for clarification.

Companies must design AI systems that seamlessly transition between automated and human-assisted support, ensuring that AI-enhanced experiences remain customer-centric and aligned with regulatory requirements.

Additionally, the role of bias detection and mitigation in AI models cannot be understated. As AI systems interact with diverse customers, the risk of unintentional discrimination increases. Organizations must regularly audit AI models, identify patterns of unfair treatment and make necessary adjustments to ensure compliance.
AI-driven personalization should enhance user experiences without reinforcing stereotypes or exclusionary practices.

This is your [AI]pilot speaking

The emergence of AI copilots – intelligent assistants that support human agents—adds another dimension to agentic AI in CX. These copilots can provide real-time suggestions, automate routine tasks and enhance decision-making. However, ensuring that these systems adhere to ethical standards and do not introduce undue influence or biases is a key regulatory concern.

Organizations must ensure that AI copilots complement human expertise rather than override it.

Finally, businesses should consider long-term AI governance strategies. Establishing AI ethics committees, investing in continuous AI education and maintaining open communication with regulators will be crucial for navigating future AI developments.

The EU AI Act will likely evolve, introducing new compliance measures, and businesses that remain agile and proactive in their AI governance will be better positioned to adapt.

Striking a balance between compliance and innovation

In conclusion, embracing responsible AI development and integrating robust oversight mechanisms, companies can ensure that agentic AI enhances customer experiences while aligning with legal and ethical standards.

This proactive approach not only safeguards businesses from regulatory risks but also strengthens brand reputation and customer loyalty in an AI-driven world. AI has the potential to revolutionize customer engagement, but its success hinges on how well businesses implement and govern its use

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