Artificial intelligence

Articles and Resources:

We set out the key concepts, legal considerations and governance questions that arise when organisations deploy agentic AI - from initial procurement through to ongoing oversight.

• Understanding Agentic AI - what it is, how it works, and how it differs from generative AI.
• From Content Risk to Conduct Risk: Identifying Agentic AI Risks - the main legal and compliance risks arising when systems can act, not just generate.
• Contracting for Agentic AI and AI Procurement - the contractual issues to address when procuring and deploying agentic solutions.
• Governance and Oversight - how governance, approvals and monitoring need to evolve when systems can initiate actions.

Given the pace of change in AI technology, these articles reflect the position as at the date of publication.

Before turning to the legal, contractual and governance issues, it is worth being clear about what we mean by “agentic AI”.

As with AI more broadly, the terminology around agentic AI is still evolving. Terms such as "agent" and "agentic system" are not always used consistently - and market labels can be broader than the underlying functionality.

In practice, the key question is not whether the vendor labels the system as "agentic", but what the system can actually do within the permissions and controls set around it.

This article gives an introductory overview of AI agents and agentic AI, covering the core concepts that inform the legal and governance issues addressed elsewhere on our hub.

With generative AI, legal risk largely centres on content: what the system produces and what it was trained on. With agentic AI, the focus shifts to conduct. That shift sits at the heart of many emerging discussions around agentic AI risks, AI liability and broader artificial intelligence risk management.

That is because agentic systems do not just generate outputs for human review. They may retrieve information, interact with tools and systems, and take action across a workflow.

The legal issues are therefore no longer limited to – for example - whether the output is inaccurate, infringing or misleading. They also extend to what the system does, how it decides to do it, and what happens if that conduct causes harm.

In short, the explainability question shifts from ‘why did the system say that?’ to ‘why did it do that?’ That latter question is harder to answer, because the answer turns on a chain of decisions, tool calls and intermediate states – often distributed across multiple components.

The shift to agentic AI has direct contractual consequences. Standard terms written for generative AI systems may not adequately address deployments where the system acts with a degree of autonomy.

This article highlights the areas where standard terms are most likely to need tailoring for agentic deployments and evolving approaches to AI and procurement.