Agentic automation
When AI agents carry out processes independently
Agentic automation – also known as agent-based automation – describes an approach in which processes are not only executed automatically, but are also controlled by AI agents depending on the context.
Typical application scenarios can be found in particular where classic automation reaches its limits – for example due to high variance, complex decision-making logic or changing framework conditions. While classic automation follows clear rules and AI-supported processes recognize patterns, the agent-based approach combines both: it evaluates situations in context, takes goals, rules and dependencies into account and derives suitable action steps from this.
This makes agent-based automation particularly suitable for dynamic specialist processes in which decisions cannot be made purely based on rules, but still need to remain traceable and controllable.
The focus is not on a single tool, but on the interaction of processes, rules, data and decision-making logic. The aim is to control complex processes flexibly without losing transparency or control. Further basics can be found in the area of process automation and AI-supported process automation.
Use cases for AI agents
Context-based case processing
Dynamic approval processes
Intelligent preliminary review of applications
Situation-dependent process control
Adaptive prioritization of tasks
Decision support in complex cases
Build a modern process architecture together with it-novum and benefit from best practices
We take a holistic view of automation – from structured processes and data-based decisions to agent-based mechanisms that act in a context-dependent manner. Agent-based automation is the next stage of development within this overall concept.