Toggle navigation Search Improved IMPROVED Analytics dashboard Top articles Top authors Who's reading? Trending Topics Discover Content Discover Companies Horizons Ideation Content Benchmarking Benchmarking and Insight Reports Lexology Back Forward Save & file View original Forward Print Share Facebook Twitter LinkedIn WhatsApp Follow Please login to follow content. Like (default) United Kingdom February 26 2025 Back Forward Save & file View original Forward Print Share Facebook Twitter LinkedIn WhatsApp Follow Please login to follow content. Like (default) Folders shared with you * * * * * MBL Seminars | 3 CPD hours Online 29 July 2025 MBL Seminars | 2 CPD hours Online 15 October 2025 MBL Seminars | 1.25 CPD hours Online 31 March 2025 PRO How-to guide Recently updated How-to guide Recently updated How-to guide Recently updated Back to Top Follow on X Follow on LinkedIn Access real-time intent data to measure your success and maximise engagement. Use advanced tools to take your marketing strategy to the next level. Measure the effectiveness of your content against peers. add to folder: Find out more about Lexology or get in touch by visiting our About page. The advent of ChatGPT in late 2022 propelled AI on to the front pages of newspapers and brought with it an explosion of interest in generative AI's disruptive potential. As we moved into 2023 and 2024, we saw the advent of many "copilot" based generative AI products along with companies looking beyond the initial hype to find use cases for the wave of generative AI tools that were coming onto the market. In this sense, whilst 2023 was the year of the hype, 2024 certainly seemed to be the year of the AI use case. The AI hype of 2025 seems to be centred around one key topic: AI agents (or agentic AI). Deloitte predicts that in 2025, 25% of companies that use generative AI will launch agentic AI pilots or proofs of concept, growing to 50% in 2027. In this article we take a look at what AI Agents are and note some of the key legal challenges that agentic AI presents. What are AI Agents? In broad terms, AI agents are systems which are capable of autonomously performing tasks by designing a workflow and calling on external tools. The goals are set by humans, but the AI agent determines how to fulfil that goal. At the foundation of AI agents are large language models (LLMs) with AI agents using the advanced natural language processing techniques of LLMs in order to comprehend and respond to user inputs step-by-step and determine when to call on external tools. AI Agents vs Monolithic LLMs Given that LLMs are at the core of AI agent systems, it is useful to consider how AI agents compare with monolithic LLMs. Monolithic LLMs predict responses based on data that they were trained on. This data is static. Monolithic LLMs don't interact with the world beyond their training data, meaning that, in isolation, the monolithic LLMs can't fetch new events or data. AI Agents, on the other hand, commonly have the following features: 1. Planning: after receiving a user prompt, AI agents can define actions needed - creating a digital plan in order to achieve the relevant goal. The advanced natural language processing techniques of LLMs are harnessed here and are revolutionising the power and ability of agentic systems. 2. Use of Tools: unlike monolithic LLMs, AI agents can interact with a variety of tools (e.g. via API) to gather data from a variety of sources in order to perform tasks effectively. Accessing these tools, and information, allows AI agents to have abilities beyond that of a static dataset (as is the constraint with monolithic LLMs). 3. Action Tasks: as the name suggests, agentic AI has agency - once the AI agent has defined the actions needed, and accessed relevant external data sources, it can execute these tasks autonomously. 4. Memory and Reflection: AI agents can have the ability to remember past interactions and behaviours, and even to perform self-reflection to inform future actions (improving the AI agent's performance over time). Multi-AI Agent Systems More advanced AI agents are likely to be multi-agent systems, where AI agents collaborate to address more complex challenges. Here, the architecture of the relevant AI system will be more complex, but could - for example - include a "lead" AI agent which "orchestrates" other AI agents in order to perform a certain task. What additional legal challenges do AI Agents present? The autonomy of AI agents, whilst bringing huge potential benefits, also presents inherent legal challenges, especially as the level of human involvement decreases (and the corresponding level of autonomy of the AI agent increases). At a high level, these legal challenges include the following: TLT Comment Whilst agentic AI presents huge opportunities and is another exciting development in the AI space, there are increased legal challenges that arise. add to folder: The GDPR - A Guide to Compliance for DPOs & COLPs - Learn Live An Introduction to Cloud Computing Contracts - Without the Jargon - Learn Live The EU AI Act - An Introduction to Risks, Compliance & More - Learn Live © Copyright 2006 - 2025 Law Business Research