What are AI agents and how do they differ from regular LLM chats
Recently, the term AI agents has been increasingly used in the context of business, automation, and personal productivity. But what does it actually mean in practice? And how does an agent differ from the AI chats based on large language models (LLMs) that many people are familiar with?
An LLM chat is not yet an agent
When you use any AI chat (based on an LLM), the workflow is always similar:
- you formulate a request
- the model generates a response
- the interaction ends there
Such a tool is reactive. It does not initiate actions on its own, does not manage a project over time, and does not make decisions without a direct user command. For example, you write: "Create a content plan of posts for Instagram for the week."
The model generates a response – and from that point on, all the work with the text, publishing, and monitoring results falls on you.
An AI agent is a sequence of actions, not a single response
An AI agent works differently. It is not "one request – one response", but a chain of interconnected steps aimed at completing a task from start to finish.
An agent can:
- receive a goal
- break it down into subtasks
- perform several actions in sequence
- interact with other services (email, spreadsheets, messengers, CRM)
- return a ready-made result or update it on a regular basis
For example, instead of a one-time request, you set up a scenario:
"Every week, analyze trends, generate 5 posts, save them to Google Docs, and send me a message in Telegram." After that, the process runs automatically, without your constant involvement.
How AI actually works – without the magic
An important part of the topic is understanding how artificial intelligence actually "thinks."
LLM models:
- do not understand meaning the way humans do
- have no intentions or consciousness
- do not "know" facts in the human sense of the word
In practice, they continuously predict the next element – a word, phrase, or action – based on statistical patterns in the data they were trained on.
If you type: "Good...", the model will most likely continue with: "...morning", "...afternoon", or "...evening."
Not out of politeness, but because that is simply how such phrases most often appear in the training data. This understanding helps avoid overestimating AI capabilities and always maintain critical thinking.
Where AI agents deliver real value
In practical terms, agents are already actively used today for:
- automating business processes
- preparing analytics and reports
- processing emails and customer inquiries
- task and project planning
- creating content for social media
- boosting personal productivity
A common approach involves multiple agents working together: one collects data, another analyzes it, and a third compiles the final result. In this format, it is no longer just a chat – it is a small system.
Important point: an agent ≠ a human
It is worth emphasizing separately: an AI agent is a tool, not an employee.
It:
- can make mistakes
- is not accountable
- does not make decisions in the human sense
That is why the focus of training is not on "magic buttons", but on correct task formulation, process logic, and result monitoring.
What's next
If you:
- are just beginning to explore AI
- want to automate routine or work processes
- have already used LLM chats but feel their limitations
the logical next step will be to dive deeper specifically into AI agents.
SkillsUp courses show step by step how to move from individual requests to building fully functional AI scenarios – without being overloaded with terminology and without programming.
AI is not about the distant future. It is about working more systematically and effectively already today.