Author: Jacquie Sprott
Over the past year, one thing has become clear: every organisation wants an AI strategy.
But when the conversation turns to AI agents, things get a little murky. What exactly is an agent?
The honest answer is that there isn’t a single agreed definition yet. Some see agents as intelligent automation. Others describe them as digital assistants that can reason, plan and act.
At Cloudwerx, we’ve spent the past year researching and designing agent-based systems. What we’ve found is that while definitions vary, most AI agents share the same building blocks. Once you understand those components, designing and scaling agents becomes far less complicated.
The Three Layers of Agentic AI
Think of an AI agent as a system made up of three layers: how it interacts with people, how it thinks, and how it gets work done.
1. The Human Layer
The first layer is where people interact with the agent.
This is the speaker. The entry point for conversations. It might appear as a chatbot, a virtual assistant, or a service interface within an application.
Importantly, this is also where organisations embed their brand voice and personality, shaping how the agent communicates with customers and employees.
2. The Thinking Layer
The second layer is where the intelligence sits.
This is what separates true agentic behaviour from simple automation. Instead of just following a predefined workflow, the agent can interpret information, make decisions and plan actions.
Within this layer, different components guide the agent’s reasoning. Some decide what step to take next, others evaluate whether an action is allowed, and others combine information from multiple sources to generate useful responses.
Together, these capabilities allow the agent to reason through tasks rather than simply execute them.
3. The System Layer
The final layer is where actions happen.
This is where agents connect to business systems to retrieve information or perform tasks. One component may act as a librarian, pulling knowledge from databases or documents. Another may act as a technician, carrying out actions such as updating records, creating cases or sending emails.
This layer turns insight into action.
Turning AI Ideas into Real Solutions
One of the biggest challenges organisations face is moving from AI excitement to practical use cases.
At Cloudwerx, we tackle this using our Tesseract methodology, which is built on human-centred design. Rather than starting with technology, we begin with the people who will actually use the solution.
Through workshops with business teams, we define the role of the agent: what it does, what knowledge it needs, what systems it interacts with, and what actions it can take.
This blueprint makes it much easier to translate ideas into a clear agent architecture that developers can build.
Why This Matters
Agentic AI can feel complex, but when broken down into clear layers to interaction, reasoning and action, it becomes far easier to design and implement.
More importantly, it allows organisations to build AI solutions that don’t just automate tasks, but understand context, make decisions and take action.
And that’s where AI starts delivering real business value.