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 it a big step further. Instead of just generating responses, AI can now also act independently, make decisions and perform tasks in your organisation's systems. 

In this article, we'll go through what AI agents are, how they differ from large language models, what their most common uses are, and how you can get started in a safe and structured way. 

What is an AI agent? 

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

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

• Interpret instructions and goals in a flexible way
• Plan and execute multi-step tasks
• Use tools and integrate with other systems (e.g. ITSM, CRM, ERP)
• Evaluate results and adjust theirbehaviourover 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 probabilities in large data sets. It is good at: 

• Answering questions
• Summarisinginformation 
• Creating text, code or analyses

However, an LLM is essentially passive and reactive. It does nothing on its own and has no built-in ability to act in systems or drive processes forward. 

What does the AI agent add? 

An AI agent often uses one or more LLMs as its ‘brain,’ but complements them with: 

• Goal management – the agent knows what it needs to achieve
• Planning – breaks down goals into subtasks
• Tool use – can call APIs and systems
• Decision logic – chooses the next step based on the outcome

A simplified analogy is that the LLM is the brain, while the AI agent is a digital employee who actually does the work. 

How do AI agents work in practice? 

A typical AI agent consists of several interacting components: 

1. Goals or tasks – for example, ‘resolve the incident’ or ‘respond to the customer query’
2. Reasoning engine – often an LLM that analyses the situation
3. Tools and integrations – systems in which the agent can act
4. Feedback loop – where the agent evaluates the results of its actions

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

Examples of uses for AI agents 

AI agents can be used in many parts of the organisation: 

IT operations and support 

• Automatic classification andprioritisationof incidents 
• Suggest and implement measures based on historical data
• Self-healing systems that identify and fix problems

Customer service 

• Round-the-clock case management
• Compilation of customer history prior to response
• Automatic escalation for complex cases

Business processes and administration 

• Reporting and follow-up
• Contract management and purchasing processes
• Coordination between multiple business systems

Analysis and decision support 

• Identify trends and deviations in real time
• Provide recommendations based on large data sets
• Support for strategic and operational decisions

What are the biggest business benefits of AI agents? 

There are many areas of application for AI agents, and just as many business benefits. With the help of AI agents, organisations can automate complex and repetitive tasks, freeing up time for employees to focus on more value-adding work. As the business grows, agents can handle increased volumes without having to increase costs or staffing levels at the same rate.  

Through continuous real-time data analysis, they also enable faster, more informed decisions, creating a consistent, reliable decision-making process. For customers, this means high availability and more accurate responses, leading to both happier customers and a more efficient service organisation.  

Risks and challenges to keep an eye on 

• Security: AI agents with system access require strong identity management and access controls
• Governance: Clear frameworks for what agents can and cannot do are crucial
• Compliance: Data protection and regulations must be built in from the start
• Human control: Critical decisions should always be reviewable and approvable

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

How do you get started with AI agents? 

For most organisations, it is no longer a question of whether AI agent technology is relevant, but how it can be implemented in a secure, controlled and business-driven way. 

A structured approach reduces risks and increases business value. Five important steps can be: 

1. Identify internal processes with a high degree of repetition and manual handling (where agents can be of benefit).
2. Define goals, responsibilities and expected impact.
3. Ensure the right architecture, security and integrations.
4. Start small with a pilot project
5. Measure, evaluate and scale up