The earliest machine learning applications were optimization tools—fraud detection, predictive maintenance, that kind of thing. Then came the second wave: language models that talked back. Ask anything, get an answer. Now, we’re entering the third wave: agents. This wave cuts closer to the core of human work—getting whole tasks done.
What is an agent?
An AI agent is a feature that can carry out complete tasks using external tools and systems. It might pick tasks from a queue—like replying to customer feedback—or respond directly to a human prompt. Some agents handle parts of a task and leave the rest for human review. Others see it through end-to-end.
Think of agents as an evolutionary step: AI grows from a tool into a teammate. At first, the teammate may be junior. Over time, it learns more and takes on greater responsibility. Just like with human colleagues, success depends on smart task delegation and proper quality control.
Generic agents in action
You can try basic general-purpose agents in ChatGPT. The Deep Research feature, for example, compiles a detailed, source-backed briefing on questions like “What are AI agents and how are they used in 2025?” It works like an actual researcher—scanning dozens of web pages, comparing facts, verifying claims, and building a coherent summary.
Read more: How does Deep Research work?
Another example: Operator, an OpenAI tool that uses a web browser to complete a user’s task—say, “Book a table for two at a Greek restaurant in Tampere for Thursday.” It’s a computer-use agent (CUA). It doesn’t need to be pre-programmed. It learns the interface on the fly and acts accordingly.
The core idea in every agent: free up people to focus on the most creative, demanding parts of their work.
Agents tailored to your work
The competitive differentiation is born when organizations train agents for their own workflows. A few examples:
- A project management agent summarizes project status and flags deviations from the plan.
- A sales support agent tracks calendars and delivers tailored customer insights just before meetings.
- A construction design assistant turns a verbal brief into a project workspace, drafts document templates, and preps communications.
The key is defining the human-agent relationship. Who instructs the agent? Who checks its work? Building your own agents means combining AI models, process knowledge, quality data, and often, a bit of app development.
Where to begin?
There are many entry points. Microsoft Copilot Studio lets most knowledge workers build simple assistants, ideal for individual use. Organization-wide agents—those that drive real automation—are usually developed through conventional system development.
Three essentials for a successful agent project:
- Right use case – the agent delivers real business value.
- Usability – people find it natural and helpful to use.
- Good data – the agent’s outputs are accurate and useful.
Costs for meaningful agent development typically start around €30.000. The pricing can be refined and discussed analytically once the use case, user expectations, and data quality are mapped out.
