From Chatbots to Autonomous Workers: What Exactly is Agentic AI?

To understand the ongoing change, we must distinguish between common generative AI (like basic ChatGPT) and Agentic AI. While a common model works on the "query-response" principle, agentic systems operate in closed loops (reasoning loops). They are given a goal, outline the steps, evaluate the result, and if they fail, they try a different path.

These systems integrate into corporate databases, financial systems, and workflows. It's no longer just a text window, but software that has "hands" – it can execute code, call APIs of other services, or perform transactions. In practice, this means that an AI agent can, within an e-commerce system, resolve a complaint, refund money to a customer's account, and update inventory, without human oversight at every step.

Capability Comparison: Agentic Capabilities in Top Models

In the field of agentic reasoning, a fierce battle is taking place among the main players. According to current benchmarks and practical tests in 2026, the situation appears as follows:

• Claude (Anthropic): Currently considered a leader in instruction following and complex planning. Its ability to work with extensive context makes it a preferred partner for legal and analytical agents.
• GPT-4o/GPT-5 (OpenAI): Excels in multimodal understanding and speed, which is crucial for agents that need to process visual and text inputs in real-time.
• Gemini (Google): Offers the best integration with the Google Workspace ecosystem, allowing agents to operate naturally within calendars, documents, and emails.

Legal Vacuum: Who Pays When an Agent Fails?

Here we encounter the main problem discussed, for example, by JD Supra. If an autonomous agent in a law firm or financial institution makes a mistake that causes financial loss, who bears the responsibility? Is it the model developer? The company that deployed the agent? Or the user who assigned the task?

Traditionally, software is considered a tool, and responsibility for its use lies with the user. However, with Agentic AI, this "wall of responsibility" is beginning to crumble. If a system exhibits a degree of autonomy that exceeds normal human control, its status may approach legal personality, or at least new forms of product liability. As analyses in Hyperdimensional state, software has so far benefited from ambiguities regarding damages caused by intangible products, but the era of powerful AI will likely end these ambiguities.

Practical Implications for Companies and Legal Departments

For in-house legal teams, the question is no longer whether to use AI, but how to manage it. Tools like Harvey (specialized legal AI) or Microsoft Copilot already enable document automation and contract analysis today. For Czech companies, this means:

• Need for governance: Companies must define clear rules on what tasks to entrust to agents and where human intervention is always required (so-called Human-in-the-loop).
• Auditability: Every step taken by an agent must be logged to allow reconstruction of the decision-making process in case of an error.
• Legal review of tools: When selecting a tool, it is necessary to monitor whether it meets data protection standards, especially if it works with sensitive client information.

Regulation in the EU and the Situation in the Czech Republic

For Czech users, the EU AI Act is the key framework. This regulation classifies AI systems according to their level of risk. Autonomous agents that make important decisions (e.g., in employee recruitment, banking, or legal processes) will likely fall into the high-risk category. This brings strict obligations for transparency, data quality, and human oversight.

Availability and language: Most top agentic models (Claude, GPT) handle Czech at a very high level, allowing their deployment in our market. However, when implementing autonomous workflows, it is necessary to consider that the legal interpretation of Czech laws may still be problematic for global models if they are not specifically fine-tuned for Czech legal practice.

Pricing Policy and Availability

When considering implementation, it is important to monitor costs:

• Claude (Anthropic): Free tier is available; Pro versions cost approximately 20 USD/month. For enterprise agentic solutions, prices are individual.
• Microsoft Copilot: Basic version free; Copilot Pro for individuals approximately 20 USD/month; Business versions are charged according to Microsoft 365 license.
• Specialized legal tools (e.g., Harvey): These tools are not for ordinary users, and their price is aimed at large corporations and law firms (in the order of thousands of dollars per month).

Conclusion: How to Prepare?

Agentic AI is not just another wave of automation; it is a paradigm shift where software ceases to be a passive tool and becomes an active participant in processes. For Czech companies and professionals, it is crucial not to wait for clear legal precedents, but to start building internal rules for AI governance today. It's not about prohibiting autonomy, but about learning to control it.