Artificial intelligence is talked about everywhere these days, but behind the marketing slogans it is easy to lose sight of what it actually is and how it can really be used. Let us explain clearly and in depth how AI works, what language models, local agents and MCP are, and where it all makes sense for a business and a household.

What artificial intelligence and a language model actually are

When people talk about artificial intelligence (AI) today, they usually mean a so-called language model (LLM, large language model). It is a program trained on a huge amount of text, from which it learned the patterns of human language. When you type a question, the model actually predicts the most likely next word (more precisely a piece of a word, a so-called token), and this is how it builds an answer word by word.

It sounds simple, but at the enormous scale of training this produces something that feels surprisingly intelligent: the model can summarize text, translate it, write an email, explain a concept or suggest code. It is important to understand that the model does not think or understand in the human sense, it works with statistical patterns. That is why it sometimes confidently makes something up (this is called a hallucination), which we will get to below.

Cloud versus a local model

Models can be run in two ways, and this difference is crucial:

  • Cloud (remote) models run on the provider’s servers. This includes ChatGPT, Claude and Gemini. You only send questions over the internet and receive answers. The advantage is huge performance and no demands on your hardware, the disadvantage is that your data leaves to a third party and usage is usually paid.
  • Local models are downloaded and run directly on your computer or server. Data does not leave anywhere, they work even without the internet and there is no usage fee. The price is that you need sufficient hardware, especially a powerful graphics card with enough memory (VRAM).

For running models locally, the most common tools are Ollama or LM Studio, which make startup very easy. A local model is useful wherever privacy matters and sensitive data must never leave the company.

The most used models

In practice you will meet two groups. Closed commercial models can only be used through the provider’s service:

  • GPT by OpenAI (known through ChatGPT)
  • Claude by Anthropic
  • Gemini by Google
  • Grok by xAI

Open models can be downloaded and also run locally:

  • Llama by Meta (one of the most widespread open models)
  • Mistral from France
  • Qwen by Alibaba
  • DeepSeek from China
  • Gemma by Google and Phi by Microsoft (smaller models suitable even for weaker hardware)

Open models come in various sizes. A smaller one runs on an ordinary PC with a better graphics card, the largest ones need server hardware. For most practical tasks today, even a mid-sized open model is perfectly enough.

What an AI agent is and how it differs from a chatbot

An ordinary chatbot only answers with text: you ask, it writes an answer and that is the end of it. An AI agent goes further, because it can actually act. An agent is in fact a language model extended by two things: by tools (for example web search, reading files, sending an email) and by a loop in which it reasons, picks a suitable tool, performs an action, looks at the result and continues accordingly.

Thanks to this, an agent not only advises but also carries out the task: it finds information, processes a document, writes a value into a system or prepares materials. It is this shift from answering to acting that turns AI into a practical helper, not just a talker.

Local agents

A local agent is one that runs at your place, on your server, and most often uses a local model. It has access to your files, network and tools, yet everything happens inside your infrastructure. No data leaves to the cloud.

That is a huge advantage wherever sensitive data is handled: at lawyers, doctors, accountants or in companies that do not want to send internal documents to foreign servers. A local agent combines the convenience of AI with full control over data. This is closely related to a home or company server and virtualization, on which such solutions tend to run.

What MCP (Model Context Protocol) is

When you want an agent to work with your tools and data, someone has to connect them. Until recently, every such connection was made in its own non-standard way, which was slow and fragile. MCP (Model Context Protocol) is an open standard that unifies this. It was introduced by Anthropic at the end of 2024 and is supported by most tools today.

The simplest way to compare MCP is to USB-C for artificial intelligence: instead of a pile of different adapters you have one universal connector. It works so that an MCP server exposes some tools or data (for example your documents, a database, a calendar or a company system) and an MCP client, that is the agent, can use them. The beauty is that an MCP server, once created, can be used by any compatible agent, so nothing has to be programmed from scratch.

How it all fits together

Imagine a company assistant running on your own server. It is made up of a local model (started via Ollama), an agent that handles tasks, and several MCP servers through which the agent safely reaches company documents and the calendar. An employee asks about a contract, the agent finds the right document through MCP, summarizes the essentials and answers. The whole thing happens inside the company and no sensitive data leaves its server. We can design and deploy exactly such solutions tailored to you.

What to watch out for

AI is a powerful tool, but it is not infallible. Before deploying, keep a few things in mind:

  • Hallucinations. The model can confidently write an untruth too. Important outputs should always be verified, not blindly trusted.
  • Privacy and data. With cloud services, consider how sensitive the information you send them is. For sensitive data, a local model is the safer choice.
  • Agent permissions. Give an agent that can act access prudently. It should have only the rights it really needs. This is related to cybersecurity principles.
  • Hardware. For local running, count on a more powerful machine, ideally with a good graphics card.

Summary

Artificial intelligence today rests on language models, which can be run in the cloud or locally. An agent turns the model into an active helper that can act, and MCP is the universal connector thanks to which the agent easily reaches your tools and data. For companies that care about privacy, it is now realistic to run your own local AI fully under control.

Interested in AI in your company, or want your own local assistant without sending data to the cloud? Get in touch, we will gladly design a tailored solution. We also handle server deployment and management as part of IT support for companies.