Previous AI models like ChatGPT operate reactively. They wait for a precise instruction (a “prompt”) and then execute it. They answer a question, summarize a text, or create an image. After that, their task is complete.

Agentic AI takes a crucial step further. An AI agent is an autonomous system that receives a higher-level objective and independently develops a plan to achieve it. It can break down complex tasks into smaller steps, use tools like the internet or other programs, make decisions, learn from feedback, and adjust its plan until the goal is reached.

A simple analogy:

• Conventional AI is like a calculator. You input 2+2, and it outputs 4. It does exactly what you tell it to.
• Agentic AI is like a project manager. You say, “Organize a birthday party for 15 people next Saturday with a budget of 300 euros.” The agent then begins to work independently: He researches locations online, compares catering prices, checks the weather forecast, creates a guest list and may even send out invitations to meetings – all without further instructions.

The functionality of AI agents is based on a continuous cycle of perception, planning, action, and learning. This can often be divided into four core components:

• Planning: Based on the main goal, the agent breaks down the task into a logical sequence of individual steps. It considers: “What do I need to do first? What information do I need for that?”
• Memory: The agent has short-term and long-term memory. It remembers what it has already done, what information it has gathered, and what it has learned from previous attempts in order to avoid repeating mistakes.
• Tool Use: This is one of the most important capabilities. An AI agent can access external tools. These include searching the internet, executing code, using APIs (interfaces) to other programs, or accessing a database.
• Reflection & Learning: After an action, the agent analyzes the result. Was the step successful? Did it bring me closer to the goal? Based on this self-assessment, they adjust their further plan and optimize their approach.

This cycle repeats until the overarching goal has been successfully achieved.

The potential applications are vast, ranging from small everyday helpers to complex industrial processes.

In everyday life:

• Personal travel assistant: “Book me a weekend trip to Lisbon for the first weekend in November. Find the cheapest flights from Hamburg, a well-rated hotel in the city center, and suggest three vegetarian restaurants.” The agent handles all the research and booking.
• Smart shopping assistant: “Plan next week’s meals, taking my nut allergy into account, and create a shopping list. Order the non-perishable items directly online from the cheapest supplier.”

In the workplace:

• Automated market analysis: A marketing team sets the objective: “Create a comprehensive report on the marketing strategies of our three largest competitors in the last quarter.” The agent independently searches press releases, social media channels, financial reports, and technical articles, summarizes the findings, and creates a finished presentation.
• Software Development: A developer could task an agent with: “Develop a simple weather app for Android that uses the current location and displays the temperature for the next 24 hours.” The agent writes the code, debugs it, and delivers the working application.
• Scientific Research: A research team could task an AI agent with analyzing thousands of scientific studies on a specific topic, identifying patterns, and formulating hypotheses for new experiments.

Despite its enormous potential, the technology is still in its early stages and faces significant challenges:

• Reliability and “Hallucinations”: AI models can still fabricate facts or make mistakes. In an autonomous system that acts independently (e.g., makes bookings), such errors can have serious consequences.
• Security: What happens if an AI agent is programmed with malicious intent or its capabilities are misused for criminal purposes? Controlling autonomous systems is a key ethical and technical issue.
• Costs: The computing power required to execute these complex, cyclical processes is currently very high and expensive.

Outlook:

Experts agree that agentic AI is the next logical step in the evolution of artificial intelligence. In the coming years, we will likely see an increase in specialized AI agents that will take over repetitive and complex digital tasks. The focus will shift from simply generating content to automating entire workflows.

The development of agentic AI will fundamentally change the way we interact with computers—moving away from individual commands and toward collaborative dialogues in which we define goals and the AI ​​autonomously determines the path to achieving them. This will not only increase our productivity but also open up new opportunities for creativity and innovation.