AI Agent

A standard chatbot is reactive: you ask, it replies, and there it stops. An AI agent is built to be proactive and persistent. Suppose you ask it to organise a team lunch. Rather than simply suggesting how you might do it, an agent could check everyone’s calendars, find a suitable time, search for nearby restaurants, make a booking, and send out the invitations, all through a chain of its own decisions and actions. It breaks a broad goal into steps and works through them.

The engine driving most agents is a large language model, which supplies the reasoning to plan and adapt. What turns that reasoning into an agent is access to tools and the freedom to use them in a loop: try something, observe the result, decide what to do next, and repeat until the job is done or it needs to check in. This loop of thinking and acting is what people mean when they describe a system as agentic.

Agents are one of the most talked about frontiers in AI, and for good reason. Done well, they promise to automate genuinely useful multi-step work, from software development to research and administration. But handing a system the power to act, rather than just advise, raises the stakes considerably: mistakes have real consequences, and questions of oversight, permissions, and trust become pressing. Many of the UK startups we cover are racing to build agents that are capable enough to be useful and constrained enough to be safe.