Throughout the recent past, we have been operating within the realm of so-called conversational AI. We wrote queries, received answers, and at best, used AI to summarize text or generate code. It was interactive, but still passive. AI waited for your command, interpreted it, and generated output. Today, however, we are entering an era where AI becomes an active participant in processes. We define this shift as the transition from chatbots to agentic systems.

What is Agentic AI and why is it different from a chatbot?

The main difference between a regular chatbot and an agentic system lies in the ability of autonomy and planning. While the classic model (like basic versions of ChatGPT or Gemini) operates on the "question-answer" principle, an agentic system works on the "goal-result" principle.

Agentic systems possess several key features that distinguish them:

• Goal Understanding: Instead of interpreting individual words, the agent focuses on what the final outcome should be.
• Planning: The agent can break down a complex task into several smaller, logical steps.
• Tool Use: The agent can decide on its own that to solve a task, it needs to use an external API, search for information in a database, or run a script.
• Iteration: If the agent finds that its first attempt failed, it can adjust its approach and try a different path.

According to analyses published by Aime, these systems no longer just handle communication, but directly perform work. This changes the definition of digital work in organizations.

Agentic Frameworks in Practice: An Example from Network Engineering

One of the most fascinating areas of application is infrastructure management. As stated by Cisco Blogs, agentic frameworks fundamentally change the perspective on network engineering. In the traditional model, an engineer had to manually configure routers, check logs for errors, and react to incidents. With agentic systems, the situation changes.

Imagine a network agent that not only monitors traffic but, upon detecting an anomaly, can independently: 1. Identify the source of the problem. 2. Propose a configuration change. 3. Simulate the impact of this change. 4. Implement it and subsequently verify network stability. This process occurs with minimal or no human intervention, dramatically reducing response time (MTTR - Mean Time To Repair).

Multi-Agent Orchestration: Digital Teams

Another significant concept is multi-agent orchestration. Instead of one huge model that tries to do everything, specialized agents are used. It's similar to a real company: you have a salesperson, an accountant, and a technician. In the AI world, you can have:

• Sales Agent: Qualifies leads and updates CRM systems.
• Support Agent: Resolves tickets, escalates issues, and monitors SLAs (Service Level Agreements).
• HR Agent: Screens candidates and schedules interviews.

These agents can communicate with each other, share context, and coordinate tasks, creating a highly efficient automated workflow that is much more flexible than old, rigid scripts.

Performance Comparison: What Does Agent Intelligence Depend On?

The success of an agentic system directly depends on its "brain," i.e., the underlying LLM (Large Language Model) that powers it. An agent requires a high degree of logical reasoning and the ability to follow instructions. Here is a brief comparison of models most frequently used for agentic tasks:

In benchmark tests focused on coding and complex reasoning (e.g., HumanEval), Claude 3.5 Sonnet has recently often been welcomed, making it a preferred choice for developers building autonomous agents.

Impact on Czech Companies and EU Regulations

For Czech and European businesses, this technology brings two main challenges: availability and regulation. Unlike the USA, where technologies are introduced very freely, in the EU we must consider the AI Act. Agentic systems that can independently make decisions about human processes (e.g., in HR) will be subject to strict rules for high-risk applications.

From an implementation perspective, it is important to know that most top models (OpenAI, Anthropic, Google) are available for the Czech market through cloud services or APIs. For Czech companies, it is crucial to ensure that the data processed by agents (e.g., customer data in CRM) remains compliant with GDPR. It is recommended to use cloud instances within the EU (e.g., Azure regions in Germany or Ireland) to minimize the risk of data transfer outside the European area.

Practical impact for you: If you are a small or medium-sized company in the Czech Republic, you don't have to build your own AI. You can use existing platforms that allow you to build agentic workflows using "low-code" tools. This will enable you to automate routine administration without needing a team of data scientists.