What are AI agents and why are they becoming increasingly important?

Since the end of 2022, interest in AI has surged. Generative AI models such as ChatGPT, MidJourney and Copilot have emerged, driving interest, investment and ambition. These tools help with faster writing, smarter searching and more efficient collaboration. Yet the use of AI often remains limited to advice, rather than actual action. A model might offer a good suggestion, but it often stays stuck in text form. You still have to process, enter or complete it yourself. Without integration with your systems, a lot of manual work remains.

AI agents address precisely this problem. They don’t just support the thinking process, they also carry out tasks. They understand goals, plan actions, adapt as needed and work within your existing systems. No demos or hype-talk, but a practical step forward. No longer just advice, but action. Fully automated and tailored to your organisation.

Imagine not just asking for a summary of a document, but having the key points automatically added to a standard report, sent to the right colleagues and followed up with a reminder in your calendar for next week.

AI agents make this possible. They don’t just carry out instructions, but also decide for themselves how best to approach a task. They can trigger other tools, take context into account and adapt if things don’t go as planned.

Instead of being a standalone tool, AI agents can be compared to digital colleagues. They work within specific systems and can independently perform actions such as processing documents, retrieving data and initiating tasks. They’re smart enough to work with guidelines or objectives without you having to spell out every step. Much like onboarding a new colleague, you provide direction, share context and set boundaries.

But even here, good collaboration requires trust, clear agreements and space for adjustments. You remain the one who maintains oversight, makes choices and is ultimately responsible for the outcome.

An AI agent is a software system that understands language, interprets goals, plans an approach, and executes actions independently. It’s not about performing a single, isolated task, but rather about managing a coherent sequence of steps. The agent analyses a request, selects the right tools, executes the action and evaluates the result. All in context, without you needing to define every detail. 

The core of an AI agent usually consists of a Large Language Model (LLM) such as GPT-4, which provides language understanding and interpretation. Around it sits a functional layer that handles execution. The agent might, for example, call APIs, retrieve data, edit documents or generate reports.

The difference from a classic chatbot lies in its autonomy. An AI agent doesn’t simply follow a predefined instruction but makes its own choices. Based on goals, guidelines and context, it determines the best route. If something fails, it looks for alternatives or requests additional information.

AI agents can also learn from their mistakes. If an initial attempt fails, say because a system is unavailable, a well-designed agent will seek an alternative. This ability to adapt makes them more powerful than pre-programmed workflows.

In practice, you often won’t work with one agent that tries to do it all, but with multiple, each handling a specific task. One agent analyses customer data, another processes documents, and a third coordinates communication. By enabling these agents to work together, you create a cohesive whole that you can tailor to your business processes. Such a setup is known as a multi-agent system. It offers flexibility, scalability, and better matches how organisations actually work. Just like human teams, it relies on clear division of tasks, coordination and mutual trust.

The rise of AI agents isn’t coming out of nowhere. Three technological developments mean organisations can already start using them responsibly today.

1.  Language models are becoming more powerful
   The latest generation of LLMs better understands what you mean, retains more context, reasons more effectively, and handles more complex instructions. This makes them suitable to act as the ‘brain’ of an agent, rather than just giving isolated answers.
   
   
2. Technology is better connected
   Organisations increasingly have flexible IT environments. Thanks to modern APIs, cloud platforms, and low-code tools, systems communicate more easily with one another. As a result, AI agents can not only analyse but also actually perform tasks within the existing landscape.
   
   
3.  Tools are emerging for building and managing
   Building AI agents is one thing. But using them responsibly also requires oversight and control. New tools make it possible to monitor agents, analyse behaviour, and intervene when necessary. Think of feedback loops, logs, dashboards, and control systems. This is essential for organisations that want not just to test AI agents but also deploy them sustainably.

Many organisations are already using applications that have features of AI agents, even if they’re not labelled that way. Think of an AI assistant that writes meeting notes, formulates action points, and automatically sends them to the relevant colleagues. Or a support bot that categorises tickets, resolves them, or escalates to the right department, complete with logs and follow-up.

You see the same development in document processing. Some systems analyse incoming contracts, extract relevant data, label documents, and archive them. All fully automated, without anyone needing to direct each step.

What do these examples have in common? They don’t just perform tasks, they also take independent action, often within existing workflows. They are early forms of AI agents. The autonomy is still limited, but the direction is clear.

AI agents require a new way of thinking. Instead of locking everything down in advance with rules and code, you give systems the space to act independently within clear boundaries.

That raises several questions:

• When is autonomy desirable, and when do you keep the ‘human in the loop’?
• How do you maintain control over security and compliance?
• How do you collaborate with a system that not only executes but also interprets?
• What does this mean for people’s roles in their day-to-day work?

At the same time, that’s exactly where the opportunity lies. Because AI agents are language-driven, they’re accessible to non-technical colleagues as well. They help accelerate innovation and reduce pressure on development capacity. And because you can start small, with one agent for one task, the barrier is lower than many people think.

AI agents aren’t a temporary hype. They’re a logical next step in how organisations use technology. They help people work smarter, fasterm and more flexibly, without pushing them aside. On the contrary: AI agents make it possible to combine human creativity and decision-making with digital execution power.

At Conclusion AI 360, we see AI agents as a structural building block in how we design processes, define roles, and ensure technology works alongside people. Want to learn more about balancing people and technology? Read our blog: The indispensable role of humanity in technological progress. 

In the next blog, we’ll answer a pressing question: What can you already do with AI agents today? We’ll show what’s possible, where to start, and how to quickly create value.