Unlike standard models that merely generate text based on a prompt, agentic systems understand goals, plan steps, use external tools, and act autonomously.
This article analyzes the economic impact, technological challenges, and security risks of this new paradigm.
The world of artificial intelligence is shifting from a phase where we merely "conversed" with models, to a phase where we "collaborate" with them. While standard LLMs (Large Language Models) such as GPT-4 or Claude 3.5 function as extremely smart encyclopedias, agentic AI functions like an employee. It can plan its own workflow, open a browser, search for data, write code, and then run it to achieve the result you defined.
Economic Impact: $450 Billion by 2028
According to an extensive survey by Capgemini Research Institute, AI agents represent one of the fastest-growing technology trends. Estimates suggest that agentic AI could generate up to $450 billion in economic value by 2028 through increased revenue and cost savings.
Interestingly, 93% of industry leaders believe that companies that can successfully deploy agentic systems in the next 12 months will gain a fundamental competitive advantage. However, most organizations are currently still in the pilot project or experimentation phase. Only 14% of companies have already implemented AI agents on a partial or full scale.
Comparison: Chatbot vs. AI Agent
To understand the difference, let's look at a practical comparison of capabilities:
- Standard AI (e.g., ChatGPT): You ask for a Paris trip itinerary. The AI will list monuments and hotels in text form.
- Agentic AI: You ask to plan a trip. The agent will search for flights itself, compare prices across different systems, book a hotel according to your preferences, add dates to your calendar, and send you a confirmation email.
Practical Applications: From E-commerce to IT
Agentic AI is changing the game in several key sectors. In e-commerce, we are no longer just dealing with product recommendations, but "agentic commerce". An AI agent can autonomously track inventory, analyze the context of a customer's purchase, and place an order for them. For the average consumer, this means the role of a personal assistant that works 24/7.
In IT and software development, agents are taking over routine tasks such as code debugging or writing test scenarios. This allows developers to focus on architecture and creative solutions, instead of spending hours fixing minor syntactic errors. This shift changes the work paradigm: instead of looking for "employees" for repetitive tasks, we are starting to define "tasks" for autonomous agents.
Crisis of Trust and Security Risks
However, with increasing autonomy comes a shadow. Data from Seoul Economic Daily and Capgemini point to an alarming decline in trust. Only 27% of organizations express trust in fully autonomous AI agents, which is a significant drop from 43% a year ago.
Why is this? The main problems are:
- Hallucinations and errors in decision-making: If an agent evaluates information incorrectly and performs a financial transaction based on it, who bears the responsibility?
- Unauthorized operations: The risk that an agent with too much authority will perform an action the user did not want (e.g., deleting data or making an unauthorized purchase).
- Leakage of sensitive data: Agents work with APIs and databases, which increases the surface for potential personal data leaks.
Availability and Price for the Czech Market
For Czech users and companies, it is important to know that tools for building agents (such as LangChain or AutoGPT) are available as open-source solutions, but require technical knowledge. Commercial platforms such as Microsoft Copilot Studio or OpenAI Assistants API are also available in the Czech Republic.
Pricing policy:
- Individual users: Subscriptions like ChatGPT Plus or Claude Pro cost approximately $20 (approx. 460 CZK) per month.
- Companies (API model): For agentic systems, the price is usually not paid as a flat fee, but per number of tokens (amount of processed information) and number of API calls. This can be expensive with intensive use, but is scalable.
Since these systems use models like GPT-4o, they also work in Czech localization, although their ability for complex planning in Czech may be slightly lower than in English.
Regulation and Future in the EU
For European companies, the EU AI Act is a key factor. The regulation clearly defines the risks associated with autonomous systems. Agentic AI that can influence human decision-making or critical infrastructure will be subject to strict rules for transparency and oversight. For Czech entrepreneurs, this means that when implementing agents, they must ensure the so-called "human-in-the-loop" mechanism – that is, the possibility that a person can intervene at any time and stop the agents.
The success of this era will not depend on how smart algorithms we build, but on how we build AI literacy – the ability of people to critically understand how agents work, what their limitations are, and how to safely collaborate with them.
Is agentic AI safe for bank accounts and payments?
In the current phase, it is recommended to use agents only with limited authority. Safe implementation requires "approval rights", where the agent can only prepare a payment, but its final approval must be done by a human.
Can agentic AI replace human work in the Czech Republic?
Rather than replacing entire professions, specific tasks will be replaced. People will have to shift from performing routine operations to managing agentic systems and strategic decision-making.
What are the best models for building your own agents?
Currently, the best results in planning and logical reasoning (reasoning) are shown by GPT-4o from OpenAI and Claude 3.5 Sonnet from Anthropic. These models have the best benchmarks in tasks requiring complex instructions.