The end of the era of mere chatbots: What is Agentic AI?

If you've become accustomed to interacting with ChatGPT or Claude in recent years, you've likely experienced the era of generative AI. This technology excels at synthesizing information and creating content. However, a standard chatbot is still "passive" – it waits for your prompt and ends after processing it. Agentic AI is different in this regard. An agent possesses the ability to plan, use external tools (such as a calendar, email, or ERP system), and, if necessary, correct its own errors without constant human supervision.

To understand how to build these systems and where to find their potential, it is crucial to understand the structure defined by Bain & Company. This structure consists of three layers: the foundational layer, the orchestration layer, and the application layer.

1. Foundational Layer: The Brain of the Entire System

The first layer consists of Foundation Models. This is the "brain" that provides the ability to reason, understand language, and perform logical deduction. Without this layer, an agent would not be able to understand the task or evaluate the outcome of its work.

In this category, models such as GPT-4o from OpenAI, Claude 3.5 Sonnet from Anthropic, or Gemini 1.5 Pro from Google dominate today. When comparing these models for Agentic AI needs, the key parameters are reasoning capability and context window (memory capacity).

Comparison of Key Players:

• GPT-4o: A top-tier universal model, excellent in logic, but with higher token costs.
• Claude 3.5 Sonnet: Currently considered a leader in programming and text nuance, which is crucial for agents when writing code for tools.
• Gemini 1.5 Pro: Unique due to its extremely large context window, allowing agents to "read" entire libraries of documentation at once.

In terms of costs, this layer operates on a pay-as-you-go model (payment for data/token volume used). For Czech companies, it is important to know that even though these models are primarily trained on English, their ability to understand the Czech language is at a level that allows for their full deployment in the Czech business environment.

2. Orchestration Layer: Logic and Planning

The second layer is probably the most important for developers and technology leaders. It is the orchestration layer (Agentic Frameworks). If the foundational model is the "brain," orchestration is the "nervous system." This layer determines how the brain connects with action. Here, it addresses how an agent breaks down a complex task (e.g., "Find the cheapest spare parts suppliers in the EU and write them an offer") into individual steps.

In this layer, we find tools like LangChain, Microsoft AutoGen, or CrewAI. These frameworks allow defining:

• Planning: How to divide a task into subtasks.
width Memory: How the agent remembers previous steps within a long operation.
• Tool Use: How the agent should call APIs of external services (e.g., web search or database access).

For the Czech technology scene, which is strongly represented by software experts, this layer presents a huge opportunity. Implementing these frameworks requires a deep understanding of processes, not just prompt engineering. Companies that can correctly configure these "orchestrators" for specific business needs will have a huge competitive advantage in the coming years.

3. Application Layer: Connecting with Reality

The third layer is the integration and application layer. This is where AI becomes a useful tool for the end-user. Here, it's no longer about models or code, but about specific interfaces (UI) and connections with existing systems (ERP, CRM, email, Slack).

Without this layer, an agent remains merely a theoretical experiment in a developer environment. For an agent to truly bring ROI (Return on Investment), it must have access to company data. For example, a customer support agent must have permission to read the order database and the complaint management system.

What does this mean for Czech entrepreneurs and companies?

For medium and large Czech companies, this model brings modularity. You don't have to buy one huge, impenetrable system. You can build your own "agentic stack" – take the best model (layer 1), connect it using a proven framework (layer 2), and integrate it into your existing e-shop or accounting system (layer 3).

Important notice – EU Regulation: When building these systems in Europe and the Czech Republic, it is essential to comply with the AI Act. As soon as an agent begins to autonomously decide on matters that impact people (e.g., approving loans or evaluating employees), it is subject to strict rules for transparency and human oversight (Human-in-the-loop). The implementation of Agentic AI must not be a "black box" – it must be auditable.