In Silicon Valley, the measure of success is changing. They no longer ask how many employees you have, but how large your "agent swarm" is. The last few months have brought a fundamental shift in the area of autonomous capabilities of artificial intelligence. AI is definitively moving from the role of a passive chatbot to the role of an active worker capable of independently performing steps in the real world.
From Chatbots to Autonomous Agents: What Is Actually Happening?
To understand the difference, it is important to define what agentic AI actually is. A traditional chatbot, such as the basic version of ChatGPT, works on a question-and-answer basis. You write it an itinerary for a trip to Ireland and it generates it for you. Agentic AI, however, goes further. If you give it the same task, it will not just write text, but will actually try to find the cheapest flights, book accommodation, and insert dates into your calendar.
This shift is driven by models that have the ability to plan and use external tools. Among the top players is Claude from Anthropic, which has become a leader in software development thanks to its programming and logical reasoning capabilities, or models from OpenAI. These systems no longer just solve words, but solve goals.
For the average user, this means that AI is ceasing to be just an "intelligent search engine" and is becoming a digital assistant. For companies, this is an opportunity for a massive increase in efficiency, but also for a radical change in cost structure. Instead of human assistants, companies are increasingly focusing on purchasing tokens – units that determine the volume of processed work within AI models.
Comparison of Top Models for Agentic Tasks
When choosing a tool for automation, it is crucial to monitor their ability for "reasoning" (logical reasoning). Here is a brief comparison of current leaders:
- Claude (Anthropic): Currently considered the top choice for programming and complex analysis. Its ability to follow instructions without "hallucinations" is somewhat higher in agentic tasks than the competition. Price: Free tier available; Claude Pro costs approx. 20 USD/month.
- GPT-4o (OpenAI): An extremely versatile model with the widest ecosystem of tools. Great for integration with other applications. Price: Free tier available; ChatGPT Plus costs approx. 20 USD/month.
- Gemini (Google): Strong in integration with Google Workspace (Docs, Gmail), which is key for agentic tasks in an office environment. Price: Free tier; Gemini Advanced within Google One for approx. 22 USD/month.
The Phenomenon of "Token Anxiety": Digital Performance Pressure
With the advent of agents, however, an unexpected side effect appears: token anxiety. It is the feeling that in order to succeed in the modern world, you must use AI to the maximum, even while you sleep. Investors and company leaders in Silicon Valley are now pushing for so-called "tokenmaxxing" – a strategy where employees are motivated to maximize the use of AI credits to accelerate product creation.
This pressure creates an unhealthy environment. People in the technology sector now monitor their agents even during their free time, at parties, or in parks. There is a feeling that if your agent is not working 24/7, you are falling behind the competition, which keeps its agents running constantly. This leads to an erosion of boundaries between work and privacy, a problem that is already manifesting itself in European labor law.
What Does This Mean for Czech Companies and Users?
This trend has several specific dimensions for the Czech market:
- Availability and language: Most top agentic tools (Claude, OpenAI) are available in the Czech Republic without restrictions. Although models understand Czech very well, their ability to autonomously interact with Czech websites or state systems (e.g., via e-Government) is still in an early stage of development and requires more tuning than in English.
- Regulation (EU AI Act): The European Union approaches autonomous systems very strictly. While in the USA the emphasis is on speed, in the EU transparency and accountability will be key. If your agent makes a mistake (e.g., incorrectly orders a flight or sends the wrong email to a client), the regulation will clearly define who bears legal responsibility – the user, or the model provider.
- Costs: For Czech small and medium-sized enterprises, the transition to agentic AI can be financially advantageous (replacing administrative work costs with token purchases), but it requires a new level of digital literacy among employees.
Identity in the Age of Automation
The biggest problem brought by agentic AI is not just fatigue, but also an identity crisis. If AI can write code, analyze data, and write reports better and faster than a human, what is left for human work? Expert Eric Weber from the recently renamed company Superhuman (formerly Grammarly) warns that people are losing their sense of expertise. Work is changing from "doing things" to "managing systems that do things".
Instead of asking how to speed up AI, we must start asking: "What is truly important to do?". The real value in the age of agents will not be in the volume of work performed (number of tokens), but in the quality of decision-making and strategic direction, which AI cannot yet fully replace.
What is the main difference between a chatbot and an AI agent?
A chatbot is a passive tool that responds to your inputs with text. An agent is an active system that has the ability to plan steps, use external tools (web browser, email, calendar), and fulfill goals without constantly entering each subsequent step.
Is the use of agentic AI in Czech safe and effective?
Models such as Claude or GPT-4o handle the Czech language excellently. However, with complex tasks (e.g., automatic processing of invoices in a Czech environment), it is necessary to reckon with a higher risk of errors due to specific Czech formats and legislation. Human oversight (Human-in-the-loop) is always recommended.
How much does it cost to run your own agents?
For regular users, it is a monthly subscription (approx. 450–500 CZK). For companies that build their own systems via API, the price depends on the number of "tokens" – that is, on the amount of data that the agent processes. The more complex the task and the more steps the agent must perform, the higher the costs.