AI agents are the next evolution beyond chatbots, and they are generating significant interest. But the term is used loosely, and the technology is still maturing. This guide explains what AI agents actually are, how they work, what they can do for UK SMEs today, and where the current limitations lie.
What Is an AI Agent in Simple Terms?
An AI agent is a system that can take actions to achieve a goal, rather than just answering questions. A chatbot responds to your prompt with text. An agent can plan a sequence of steps, use tools (like searching a database, sending an email, updating a CRM record), and execute those steps to accomplish a task. The difference is between telling you how to do something and doing it for you.
For example, if you ask a chatbot "draft an email to follow up with a prospect," it gives you text to copy and send. If you ask an agent the same thing, it could draft the email, find the prospect email address in your CRM, schedule the email to send at an optimal time, and log the interaction in your CRM. The agent takes action across multiple systems.
An AI system that can plan steps, use tools, and take actions to achieve a goal autonomously or semi-autonomously. Unlike a chatbot that only generates text responses, an agent can interact with external systems, make decisions, and execute multi-step workflows.
How Do AI Agents Work?
AI agents work by combining several capabilities. They use a large language model (LLM) as their reasoning engine, the same technology behind ChatGPT. See our guide on LLMs explained for background. They have access to tools: APIs, databases, email systems, file storage, and other services they can interact with. They can plan: given a goal, they break it into steps and execute each one. They can observe: after taking an action, they check the result and adjust their plan if needed.
The process is: you give the agent a goal, the agent plans the steps needed, it executes each step using available tools, it checks results and adapts, and it reports back when done or when it needs human input. This is called an agentic workflow.
What Can AI Agents Do for UK SMEs Today?
Practical agent use cases for SMEs today include research compilation: an agent can research a topic across multiple sources and compile a structured briefing. Document processing: an agent can receive a document, extract key information, update relevant systems, and file the document. Monitoring and alerting: an agent can watch for specific events (a stock level dropping, a customer complaint) and take predefined actions. Meeting coordination: an agent can schedule meetings, send invitations, and prepare agendas based on context.
These are multi-step processes that currently require a person to coordinate several tools manually. An agent automates the coordination while a human reviews the outcome.
What Are the Limitations of AI Agents?
AI agents have significant limitations at their current stage of development. Reliability: agents can make mistakes in planning or execution, sometimes taking unexpected actions. Tool integration: connecting agents to your specific business systems requires technical setup and may not be straightforward. Oversight complexity: because agents take actions rather than just generating text, the risk surface is larger and harder to monitor. Cost: running agents that make multiple LLM calls and tool interactions can be expensive at scale. Maturity: agent frameworks are evolving rapidly, meaning implementations may need frequent updates.
For these reasons, most SMEs should treat AI agents as experimental technology to explore but not yet rely on for critical business processes. The safest approach is to start with agents that produce output for human review rather than agents that take direct action.
How Do AI Agents Compare to RAG and LLMs?
These three concepts build on each other. An LLM is the underlying language model that generates text. RAG adds the ability to retrieve information from your business data before generating (see our guide on RAG in simple terms). An agent adds the ability to take actions and use tools. In practice, an agent might use an LLM for reasoning and RAG for retrieving business data, combining both to take informed actions.
For most SMEs, the progression is: start with LLM tools (ChatGPT, Copilot), add RAG when you need AI to work with your business data, and explore agents when you want to automate multi-step workflows. Each step adds complexity and capability.
How Should UK SMEs Approach AI Agents?
The practical approach for SMEs is cautious experimentation. Explore agent capabilities using tools like ChatGPT with agent features, Microsoft Copilot Studio, or platforms like AutoGPT. Start with low-risk tasks where the agent produces output for human review, not direct actions. Do not deploy agents for tasks with financial, legal, or customer-facing consequences without extensive testing. Monitor agent behaviour closely and have clear escalation paths when things go wrong.
The BCC found that 71% of SMEs have not identified a need for AI. For agents specifically, the identified need is smaller because the technology is newer and less proven. Most SMEs should focus on simpler AI tools first and explore agents once they have mature AI usage and the technical capability to manage them. See our guide on getting started with AI for the right sequence.
If you want to explore AI agents for your business, book a free discovery call with our team. We help UK SMEs navigate the AI landscape pragmatically. See our services for details.