AI agents explained: What they are and why they matter right now

Why AI can feel overwhelming right now

Most businesses aren’t new to digitalisation. For years, they’ve relied on technologies that deliver structure, predictability, and control to daily operations:

• ERP systems
• Workflows
• Automation
• Integrations

These systems behave predictably: they follow rules, execute predefined steps, and deliver the same outcome every time.

AI doesn’t work like that, and that’s the source of both its potential and its complexity.
If traditional software behaves like a rule‑following machine, AI agents behave more like interns: they understand goals, make decisions along the way, and sometimes even surprise you with how they get there.

From Automation to Agents: What has changed?

Traditional automation is rigid and rule‑based by design.

You define:

• If X happens → do Y
• If something goes wrong → stop or escalate

This works very well until the real world becomes too complex for fixed rules.

AI agents represent a shift:

• From fixed rules → to understanding context
• From single actions → to choosing the best action
• From “execute” → to “reason, then act.”

A helpful way to think about an AI agent is as a digital colleague.

• It understands what’s happening in the moment
• It knows which tools it can use
• It follows the rules and boundaries you set
• It supports people instead of replacing them

This doesn’t make agents magical, but it does make them fundamentally different from traditional automation. Instead of simply following a script, they can decide how best to reach a goal.

What makes something an “AI agent”?

An AI agent isn’t a single technology. It’s a combination of components working together.

1. A language model, aka the “brain”
 At the core sits a large language model (LLM), often described as the agent’s “brain.”

 This is what allows the agent to:

• Understand text and instructions
• Interpret intent
• Generate responses or suggestions

It’s important to be clear: The model does not know your business. It only understands patterns and language.

2. Context or what the agent is allowed to know
 Context gives the agent relevance and may include:

• ERP data
• Roles and permissions
• Process rules
• History or previous interactions

Without context, an agent is generic. With context, it becomes useful and safe.

3. Tools, skills, and actions
 This is what enables an agent to take action.

 For example:

• Read or update data in Dynamics 365
• Trigger a workflow
• Call an external service
• Ask a user for clarification

An AI agent doesn’t act freely; it can only use the tools it has been given.

4. Triggers and boundaries
 Agents don’t run automatically in the background; they are activated by:

• A user request
• A system event
• A defined condition

 And they operate within boundaries:

• What they can decide
• What requires approval
• What must be escalated

When I first worked with AI agents in a customer service setup, the moment it clicked was watching the agent handle incoming emails. It read the message, understood the customer need, created a ticket, and routed it to the right team, without a single rule like ‘if invoice → send to finance.

Sander van Koppen, Senior Solution Engineer, BE-terna Denmark

It wasn’t following a script. It was understanding the request and taking the next logical step. That’s when I realized: this isn’t automation anymore. It’s a digital colleague that can reason.”

This shift from executing tasks to reasoning about tasks is what makes AI agents so transformative.

Autonomy doesn’t mean losing control

One of the biggest concerns around AI agents is autonomy. 

Questions like these often come up:

• Will it make decisions on its own?
• Can it change data without approval?
• Can it act in unpredictable ways?

In practice, autonomy is designed, not assumed, which means most agents start as:

• Assistive
• Recommendation-based
• Human-in-the-loop

These modes ensure that people stay in control while the agent learns to operate safely.

Autonomy increases gradually and only where it makes sense. This is especially important in business systems, where:

• Data quality matters
• Compliance matters
• Trust matters

AI agents are only as autonomous as you design them to be, and maintaining control is a deliberate part of that design.

What AI agents are not

Clarity often comes from defining what something is not.
AI agents are not:

• A replacement for ERP systems
• A shortcut around good process design
• A “plug-and-play” solution that works without the setup of refinement
• Artificial intelligence that automatically understands your business context

They are a capability, not a complete solution on their own.

AI agents become valuable only when combined with strong data, well‑designed processes, and clear business goals.

Why understanding this makes a difference

If you jump into use cases without first understanding how agents work, you risk the following:

• Overestimating what AI can realistically deliver
• Underestimating the importance of governance and data quality
• Creating solutions that look impressive but don’t scale

Understanding the basics gives you something far more valuable than inspiration: good judgment.
And that’s exactly what companies need when navigating AI today. Solid judgment becomes the difference between experimental pilots and AI that truly transforms operations.

What comes next

Now that we’ve clarified what AI agents are, the next question becomes unavoidable: Where do AI agents create real value, and where do they not? That’s exactly what we’ll explore in the next blog post about AI.