Agentic AI refers to AI systems based on Large Language Models (LLMs) that are capable of not only executing complex tasks, but also autonomously coordinating and managing them over extended periods of time. Rather than relying on isolated AI models or rigid automation solutions, agentic systems pursue goals, break down tasks into sub-processes, prioritize actions, and dynamically adapt their behavior to changing requirements.
Unlike traditional automation approaches or purely generative AI systems, Agentic AI responds to changes in real time, makes autonomous decisions, and orchestrates complex workflows across different systems. At its core, Agentic AI is characterized by the following capabilities:
- Autonomous decision-making
- Dynamic adaptation of processes
- End-to-end automation of complex workflows (Agentic Workflows)
- Integration of existing systems, data sources, and tools
Agentic AI therefore forms the foundation for a new generation of process automation that combines flexibility, scalability, and intelligent decision-making. This enables organizations to focus more strongly on strategic and value-generating activities. In practice, implementation is often realized through specialized AI agents that handle specific tasks or roles within an agentic system.
