What exactly is Agentic AI? The difference between a chatbot and an agent

To understand this shift, it's important to define the difference between a common large language model (LLM) and an agentic system. A traditional chatbot, such as the basic version of ChatGPT or Claude, operates on the principle of a prompt: the user asks a question, the model generates an answer. If you want the chatbot to do something (e.g., book a flight), you have to give it instructions step by step.

Agentic AI works differently. Instead of individual questions, you give it a goal. For example: "Plan a business trip to Tokyo for me, book a highly-rated hotel in the city center, and send me the itinerary to my calendar." The agentic system will break down this goal into subtasks, search for information online, visit the hotel's website, perform the transaction (if you give it permission), and finally execute the action in another software.

This process requires three key capabilities that common models often lack or have to a limited extent:

• Reasoning: The ability to logically evaluate which step will lead us to the goal.
• Planning: Creating a hierarchy of tasks and the ability to correct its plan if it encounters an obstacle.
• Tool Use: The ability to interact with APIs, a browser, a calculator, or a database.

In benchmarks that measure AI's ability to solve real-world problems, we see a huge leap in agentic systems. While standard models excel in MMLU-type tests (general knowledge), agentic systems are tested on platforms like SWE-bench, which simulate the real work of software engineers. Here, it becomes clear that the ability to autonomously fix code errors is what distinguishes a mere text generator from a true digital worker.

Technological Pillars: Who benefits from this shift?

The transition to agentic AI requires incredible computational power. Agentic cycles (so-called reasoning loops) mean that the model has to "think" repeatedly about the same task, which consumes much more processing time than a one-time answer. According to an analysis by The Globe and Mail, the main winners in this regard are hardware manufacturers and cloud infrastructure providers.

Hardware: The Brain of Agentic Systems

The first key player is Nvidia. Their graphics processing units (GPUs) are de facto the standard for both training and running these models. Without the massive parallel processing that GPUs offer, agentic systems would be too slow for real-world deployment. Another crucial player is Broadcom, which dominates in specialized chips (ASIC) and network infrastructure that allows huge amounts of data to flow between servers without latency.

Cloud Infrastructure: Where Agents "Reside"

For an agent to work, it must have access to tools. This means it must be integrated into cloud environments. Here, giants like Microsoft (Azure), Google (Google Cloud), and Amazon (AWS) dominate. These providers don't just sell computing power, but entire ecosystems where an agent can connect to emails, calendars, databases, and other corporate applications.

Practical Impact: What does this mean for you?

For the average user, this means that software will stop being just a "passive tool" and become an "active assistant". Instead of manually copying data from emails into Excel, an agent will do it for you in the background. For companies, this represents a huge opportunity to automate processes that previously required human logic, not just simple scripts.

Pricing Policy and Availability: Currently, the price for accessing agentic capabilities varies depending on the usage.

• B2C (For individuals): Subscriptions like ChatGPT Plus or Claude Pro cost approximately 20 USD (approx. 460 CZK) per month. These models are already starting to implement basic agentic functions (e.g., data analysis in files).
• B2B (For companies via API): Here, the price is not paid as a flat fee, but per token (the amount of text processed). Because agentic systems "think" in endless loops, their operation can be significantly more expensive than regular chatbots. Companies must anticipate higher costs for operational AI.

Context for Czechia and the EU: In the Czech Republic and throughout the EU, we must view these systems through the lens of the EU AI Act. Agentic AI brings a higher risk because it can autonomously make decisions that impact people (e.g., in banking or employee recruitment). Regulation will be crucial to ensure that these systems are transparent and that it is clear who bears responsibility for an error made by an autonomous agent. In terms of availability, the Czech market is fully integrated into global services – tools like Microsoft Copilot or Google Gemini are available in Czech, and their agentic capabilities are gradually expanding to our linguistic environment.

Conclusion

Agentic AI is not just another chatbot improvement. It is a fundamental change in how we will interact with computers. While the first wave of AI taught us how to talk to computers, the second wave will teach us how to delegate work to them. For tech investors and everyday users alike, the question is not only what AI can do, but how much we will be able to trust it.