Agentic Automation
Agentic Automation – The New Era of Automation
Through LLMs and Agentic AI, a new category of automation-capable machines is emerging.
- Interpret information (understand context)
- Generate content (texts, decisions, steps)
- Pursue goals and plan steps independently
- Systems act like digital specialists or operators.
- Processes are not just "automated," but self-steering.
- Efficiency potential increases exponentially, not linearly.
What is the core difference from "Intelligent Automation"?
The jump from pure intelligence to capabilities that approach human decision-making.
Not only repetitive work is automated...
...but also decision-making + the resulting actions.
With significantly less oversight and manual control than before
Intelligent Automation
Intelligent Automation – The first Evolutionary Stage
The introduction of Artificial Intelligence that imitates parts of human thinking.
Some examples
- Predictive Analytics
- Natural Language Processing (NLP)
- Voice Analytics
- RPA (Robotic Process Automation)
- Machine Learning, Deep Learning
Characteristics
Models can learn, but only within a clearly defined, narrow context.
No true content interpretation or generative capabilities.
Focus: Human intelligence is imitated—but not decision-making power.
Rule-Based Automation
Classical (Rule-Based) Automation
Functions primarily through human control logic that is explicitly programmed.
Use cases
- Basic Automation
- Rule-based Workflows (e.g., invoice processing, routing, extraction)
- Process Mining
Characteristics
Rigid rules & low flexibility
No understanding of context
