Rule-based Bots
Rule-based Bots are automation agents that execute predefined logic paths based on explicit conditions. While effective for deterministic tasks, they traditionally struggle with ambiguity and unstructured data inputs.
Evolution: From Steps to Decisions
Traditional robotic-process-automation (RPA) excels at automating repetitive, linear “steps” (e.g., data entry, button clicks) but lacks native capabilities for complex decision-making. Recent advancements in Image Decision Models bridge this gap by enabling bots to interpret visual context directly.
Key Advancements
- Direct Unstructured Data Decisions: New models allow RPA systems to make decisions based on visual inputs (forms, scans, screenshots) without requiring structured data extraction first.
- Visual Context Interpretation: Bots can now analyze Screenshots and Scans to determine workflow paths, moving beyond simple coordinate-based clicking.
- Reduced Fragility: By relying on visual understanding rather than rigid DOM selectors or OCR pipelines, automation becomes more resilient to UI changes.
Integration with Jev Image Decision Models
The integration of Jev Image Decision Models represents a shift toward “direct” decision-making capabilities. This approach allows bots to process visual data as a primary input for logic gates, rather than treating it as a secondary verification step.
- Core Concept: Automating the “decision” node in flowcharts, not just the “action” nodes.
- Application: Handling Forms and Scans where traditional OCR fails or is too slow.
- Reference: Jev Image Decision Models for RPA: Direct Unstructured Data Decisions
References
- Witteveen, S. Image Decision Models for RPA: Forms, Scans and Screenshots. https://www.youtube.com/watch?v=L8YxigQoLaM