AI agents and MCP: what they mean for your business

Most businesses met AI through chat: a question in, an answer out. The next step is already in use. AI agents do more than answer questions. They do work: fetching data, filling in forms, updating records and closing cases in the systems a business already uses.

What is an AI agent?

An agent is AI that has been given a goal, access to tools and rules about what it may do. Instead of a staff member copying details from an email into a booking system, the agent can read the request, look up the customer, propose a booking and ask for approval before it is confirmed.

The difference between an agent and traditional automation is flexibility. Traditional automation follows fixed rules and stops when something unexpected happens. An agent can handle varied requests, which is exactly why it needs a clearer frame.

What is MCP?

MCP (Model Context Protocol) is an open standard that Anthropic introduced in 2024 and that the major AI companies now support, including OpenAI, Google and Microsoft. It defines a common way for AI to connect to systems: which data it may read and which actions it may take.

The simplest way to think of MCP is as an integration for AI. Instead of building a separate connection for every AI tool, a business sets up one MCP server on top of its system. Agents such as ChatGPT, Claude or the business's own agents can then use it, with the same access control.

Where do agents deliver the most?

• Requests arriving by email or chat that need to be classified, answered or recorded in a system.
• Bookings, orders and changes that require lookups in more than one system.
• Documents such as invoices, receipts and applications that need to be read and recorded.
• Internal questions from staff that can be answered from handbooks, contracts and procedures.
• Reports and summaries that are assembled by hand today from several systems.

What needs to be clear first

• Access: an agent should only see and do what its role requires.
• Approval: actions that cost money or change customer data should go through human approval, at least at first.
• Logging: every action the agent takes needs to be traceable, so you can see what was done and why.
• Data: where data is processed and stored, and whether it is used to train models.
• Evaluation: measurable criteria for when an answer or action counts as correct.

Regulation matters too. The EU AI Act is being applied in phases and is expected to reach Iceland through the EEA Agreement. Most everyday business solutions fall into a low-risk category, but it pays to record from the start which AI is used and for what.

How to start sensibly

Start with one process that is time-consuming, repetitive and well defined. Measure what it costs today, build a focused prototype on real data and let the agent work with human approval for a few weeks. You then know whether the benefit is real before going further.

At Novamedia we use AI in our own products every day. In Spjallbox, AI drafts replies that staff review and send, and Tímabox reads receipts from a photo. The same approach applies to agents: start small, measure and build trust before the agent gets more autonomy.