Staying Ahead in the Age of Agentic AI

Agentic AI - technology that can autonomously gather data, make decisions, and take actions across digital environments - heralds a profound shift in how value is created and consumed across digital platforms.

Unlike forms of AI that generate text or images based on prompts (generative AI), agentic AI can interact with a host website or system without relying on human-driven clicks or navigation, and proactively roam the digital environment. For businesses built around data, user communities, and digital marketplaces, agentic AI signals an exciting opportunity to reshape existing models.

However, the systems can harvest your platform’s value, without engaging with your monetisation mechanisms. This article examines three critical challenges that agentic AI poses to platform-based enterprises, followed by strategic and legal responses designed to safeguard long-term relevance and revenue.

For forward-thinking organisations, agentic AI also presents an opportunity: by establishing licensing programmes and forging strategic alliances with AI providers, you can expand your role in the digital economy rather than be sidelined by it.

For further reading, see our article on the rise of AI agents here - The rise of AI agents - TLT LLP.

1. Platform Disintermediation

Agentic AI systems have the technical capacity to access and extract information from your site, sometimes at scale, without returning users to your platform’s front-end. Over time, such disintermediation could degrade traditional advertising or transaction-fee models, as visitors no longer need to engage with your site directly.

Key Challenges:

• Traffic erosion: when AI agents extract and repackage information, user visits to your website may decline significantly, which can undermine advertising-based revenue models.
• Data commoditisation: content or data you have invested in producing or curating, such as reviews, and other user-generated content, can be used elsewhere without recognition or compensation, undermining your unique value proposition.
• Disintermediation: AI agents will increasingly bypass marketplaces.

Strategic Protections:

• Strengthening terms of service: craft language explicitly prohibiting unauthorised scraping, data extraction or use by autonomous systems. This provides a legal footing should you need to enforce violations.
• Technical Barriers: invest in bot-detection and other anti-bot technologies, such as challenges, IP blockers and rate limiting, that help identify and block suspicious activity, ensuring that AI-driven scraping or interactions remain within your control.

2. Advertising Revenue Models

As agentic AI changes the way in which some visitors arrive at and use your platform, advertising-based or transactional revenue models may be negatively affected. With fewer direct page views, impressions, or clicks, your ability to monetise via ads diminishes.

Key Challenges:

• Advertising effectiveness: automated tools may not engage with advertisements, undermining impression-based revenue models and threatening a critical revenue pillar for many digital platforms.
• Compromised analytics: metrics like bounce rates or conversion may lose credibility if a significant share of traffic comprises AI-driven sessions.
• Transactional fees drop: if AI agents do not use your interface, or if they bring transactions directly to competing channels, your transactional fees will be impacted.

Strategic Protections:

• Paid APIs: shift from relying solely on front-end user visits to a multi-faceted model where AI agents pay for data access, transforming your content into a licensable resource for AI agents to use.
• Enterprise partnerships: collaborate directly with major corporations wishing to leverage your data or user ecosystem, creating structured deals with clear usage boundaries where this aligns with your commercial strategy.

3. Data Ownership and Licensing

For agentic AI tools, data is everything. If your business hosts curated user reviews and user-generated content, or structured datasets, you have content with notable market value. However, such content can be consumed by AI systems with little benefit flowing back to you unless you establish robust data rights and licensing frameworks.

Key Challenges:

• Model training: your data could be ingested into large-scale AI models without your knowledge or consent, transforming your platform into a data source for someone else’s AI model.
• Differentiation: if your proprietary content is replicated in other AI tools, your brand’s uniqueness diminishes, impacting brand loyalty and your intellectual property rights.

Strategic Protections:

• Licensing agreements: require explicit consent and, where appropriate, financial compensation, for any use of your platform’s data in AI model training or automated processes.
• Machine-readable signals: adopt metadata that indicates “no AI use” or “restricted use” (akin to robots.txt files but specifically for AI), sending a clear message to automated systems regarding permissible data extraction.
• Intellectual property: ensure suitable positioning around your proprietary assets, including being suitably positioned in respect of database rights, trade secrets, and trade marks.

Conclusion: Seizing the Advantages of Agentic AI

You must adopt rigorous legal and technical strategies to maintain ownership, credit, and revenue, to prevent agentic AI from transforming your platform into a mere data source for AI scrapers.

In essence, success in the age of agentic AI depends on your capacity to secure your key assets, reinvent your revenue streams, and adapt to a rapidly shifting legal environment

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 June 2025. Specific advice should be sought for specific cases. For more information see our terms & conditions.