Agentic AI: A Short Introduction and Key Legal Considerations

According to recent industry analyses, the global agentic AI market could grow at over 40% annually, with forecasts suggesting it may exceed USD $50 billion by 2030 (source - AI Agents Market Size & Share Analysis, Growth Report - 2030).

This rapid expansion underscores just how transformative agentic AI - software that autonomously makes decisions, takes actions, and learns from encounters - could be for businesses, consumers, and regulators alike. It also underscores how impactful agentic AI will be, given the ability of agentic AI to extract value from platforms without engagement, in an environment where the existing legal frameworks require change to address agentic AI.

Unlike simpler automated tools, agentic AI exploits dynamic data inputs, can change its operational strategies in real-time, and may interact with digital ecosystems in a manner comparable to human users.

Key takeaway: The ability for agentic AI to make decisions autonomously makes immediate compliance challenges. Our specialist advisors can support you to stay ahead of this technology’s legal implications.

For further reading, see our articles on The rise of AI agents and Staying Ahead in the Age of Agentic AI.

What is Agentic AI?

Agentic AI refers to systems that do more than simply process tasks based on narrow instructions. These systems autonomously identify objectives, navigate online platforms, and make independent choices to achieve predetermined goals, distinguishing them from traditional rule-based automation.

These programmes do not wait passively for new orders; rather, they adjust and expand their capabilities as fresh data emerges or as their understanding of a task evolves. In practice, agentic AI can autonomously post on social media, manage transactions on ecommerce sites, or even data-mine multiple sources to compare products, often without direct human supervision.

How is agentic AI different from generative AI?

Where generative AI aims to create new content - ranging from text and music to intricate designs -agentic AI has a broader operational scope.

Generative AI responds to user prompts: for instance, it will compose marketing copy or produce artwork based on a query. However, it cannot act autonomously to achieve set goals.

In contrast, agentic AI works by connecting with generative AI, using an LLM as its “brain”. Agentic AI can proactively roam the digital environment. It might schedule appointments, purchase goods, or gather information without requiring user interaction. It thus challenges the conventional path of user-driven navigation on digital platforms, potentially resulting in less direct user engagement with the websites or apps involved.

AI Agents vs. Agentic AI

The terms “AI agents” and “agentic AI” frequently get mixed up.

AI agents refer to systems designed to automate simple, repetitive tasks, such as managing calendars or handling basic customer service queries. They act as “virtual helpers”, doing exactly what you tell them to do, therefore acting with limited autonomy and requiring human input.

 Agentic AI, however, refers to a type of AI that is focused on autonomy. Agentic AI can make decisions, take actions, and learn independently to achieve goals, interacting with external software tools. It can think, reason, and adapt to changing circumstances without the need for constant human direction.

To summarise, AI agents are task-specific tools following set instructions - while agentic AI comprises of systems with higher autonomy to make independent decisions.

Why does the rise of agentic AI Mark a disruptive shift?

Agentic AI changes the balance of power in the online environment, given its ability to execute tasks with incredible efficiency. Businesses reliant on direct user engagement - through ads, access fees, or data-driven interactions – may be displaced if agentic AI reroutes consumers. For instance, a price comparison service might lose traffic if agentic AI tools can instantly assess multiple market websites and place orders based on the best available deals.

At the same time, this technology also reduces user friction: agentic AI now automatically completes tasks that once required several minutes or hours. That efficiency, while beneficial to end users, raises concerns for businesses whose revenue streams rely on controlling and monetising consumer journeys.

Please see further on this topic in our article Staying Ahead in the Age of Agentic AI.

Key Legal Issues

There are a multitude of different legal issues arising from the growth of agentic AI, including:

• Data Usage and Ownership - agentic AI leverages vast amounts of data. This creates immediate legal compliance questions around the legality of data scraping, such as when automatic collection violates a platform’s terms of service. 

• Data Privacy and Compliance - as it navigates diverse digital platforms, agentic AI could inadvertently collect personal data, leading to potential breaches of data protection law. Companies deploying such tools must ensure they implement robust data protection and consent frameworks, ensuring clear role allocation.

• Liability and Accountability - when agentic AI causes harm (including financial and reputational), determining liability presents a legal grey area. Legislative bodies are starting to re-examine accountability structures to address this challenge.

• Contractual Enforcement - many platforms stipulate rules against unapproved automated use. Businesses must adopt more advanced detection methods to enforce such terms, with a view to potentially engaging in litigation.

• Regulatory Developments - regulators worldwide, including in the EU, have explored and implemented AI regulations specifically targeting AI usage, transparency, and ethical concerns. Organisations must remain agile to align with new rules and avoid penalties or reputational harm.

• Governance – organisations investing in or deploying agentic AI must establish a suitable governance framework to oversee model development, autonomous decision-making processes and limitations, and to ensure that the use of these systems align with commercial objectives and risk tolerance. See our top tips for getting AI governance right here: Five top tips for AI governance.

Conclusion

Agentic AI brings enormous promise for efficiency and innovation, but it equally disrupts traditional digital frameworks. Its ability to learn, adapt, and act independently means that legal issues will demand careful attention, including adherence to regulation.

As this shift is now present, staying informed and proactive about regulatory and contractual obligations is critical in ensuring that this ground-breaking technology delivers its benefits without undermining foundational business or consumer interests. Businesses should ensure they are future-proofing their position from a compliance angle as regulation emerges.

For more information on how we can help you with your AI journey, please get in touch.

Authors: Michelle Sally, Tom Sharpe, Sol Pearson

This publication is intended for general guidance and represents our understanding of the relevant law and practice as at July 2025. Specific advice should be sought for specific cases. For more information see our terms & conditions.