What are AI agents? – everything you need to know!

AI agents have quickly become one of the most talked-about areas in artificial intelligence. Many organisations already use generative AI, often in the form of large language models (LLMs) that power chatbots and other interactive tools. With AI agents, you can take this a significant step further. Instead of just generating responses, AI can now act independently, make decisions, and perform tasks within your organisation’s systems.

In this article, we explore what AI agents are, how they differ from large language models, their most common use cases, and how to get started in a safe and structured way.

What is an AI agent?

An AI agent is an intelligent system that can perceive its environment, reason about information, and take action to achieve a defined goal – often without continuous human intervention.

Unlike traditional automation solutions, AI agents are dynamic and adaptive. They can:

• Interpret instructions and goals flexibly
• Plan and execute multi-step tasks
• Use tools and integrate with other systems (e.g. ITSM, CRM, ERP)
• Evaluate results and adjust their behaviour over time

AI agent vs LLM – what is the difference?

To understand the value of AI agents, it is important to distinguish them from large language models (LLMs), such as ChatGPT.

What is an LLM?

An LLM (Large Language Model) is trained to understand and generate text based on patterns in large datasets. It excels at:

• Answering questions
• Summarising information
• Generating text, code, and analyses

However, an LLM is fundamentally passive and reactive. It does not act on its own and has no built-in ability to interact with systems or drive processes forward.

What does the AI agent add?

An AI agent typically uses one or more LLMs as its “brain,” but extends them with:

• Goal management – understanding what needs to be achieved
• Planning – breaking down goals into smaller tasks
• Tool usage – calling APIs and interacting with systems
• Decision-making logic – determining the next step based on outcomes

A simple way to think about it is that the LLM is the brain, while the AI agent is a digital employee that actually gets the work done.

How do AI agents work in practice?

A typical AI agent consists of several interacting components:

1. Goals or tasks – e.g. “resolve the incident” or “respond to the customer query”
2. A reasoning engine – often an LLM that analyses the situation
3. Tools and integrations – systems where the agent can take action
4. A feedback loop – where the agent evaluates outcomes and adjusts accordingly

Through this loop, the agent can work iteratively until the goal is achieved or escalate the task to a human when needed.

Examples of AI agent use cases

AI agents can be applied across many parts of an organisation:

IT operations and support

• Automatic classification and prioritisation of incidents
• Suggesting and implementing actions based on historical data
• Self-healing systems that detect and resolve issues

Customer service

• 24/7 case handling
• Compiling customer history before responding
• Automatic escalation of complex cases

Business processes and administration

• Reporting and follow-up
• Contract management and procurement processes
• Coordination across multiple business systems

Analytics and decision support

• Identifying trends and anomalies in real time
• Providing data-driven recommendations
• Supporting strategic and operational decisions

What are the key business benefits of AI agents?

AI agents can automate complex and repetitive tasks, freeing up employees to focus on higher-value work. As organisations grow, agents can handle increasing volumes without requiring proportional increases in cost or staffing.

By continuously analysing data in real time, they also enable faster and more informed decision-making, leading to more consistent and reliable outcomes. For customers, this translates into higher availability and more accurate responses—resulting in improved satisfaction and more efficient service operations.

Risks and challenges to consider

• Security: Agents with system access require strong identity and access management
• Governance: Clear boundaries for what agents can and cannot do are essential
• Compliance: Data protection and regulations must be built in from the start
• Human oversight: Critical decisions should always be reviewable and approved

Successful adoption of AI agents therefore requires both technical expertise and clear governance.

How do you get started with AI agents?

For most organisations, the question is no longer whether AI agents are relevant, but how to implement them in a secure, controlled, and business-driven way.

A structured approach helps reduce risk and maximise value. Five key steps include:

1. Identify internal processes with high levels of repetition and manual work
2. Define goals, responsibilities, and expected outcomes
3. Ensure the right architecture, security, and integrations
4. Start small with a pilot project
5. Measure, evaluate, and scale