Agentic RPA Orchestration With Self-Healing for Higher Autonomy
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Solution Overview
Problem
Existing AI agents and RPA robots operate independently and lack effective interoperability, leading to inefficiencies and limited autonomy.
Innovation Solution
A unified system integrating AI agents and RPA robots with self-healing capabilities, allowing them to dynamically select and execute tasks, learn from human interactions, and escalate when needed, while being managed by a conductor application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI agents and RPA robots operate independently as separate applications, then each can maintain its own functionality and decision-making capabilities, but interoperability between them is limited and requires complex integration mechanisms
Solution Approach 1:
The patent merges AI agents and RPA robots into a unified system architecture where both can operate as independent components while sharing common resources, communication protocols, and coordination mechanisms. This allows them to maintain individual functionality while achieving seamless interoperability through a standardized framework that reduces integration complexity.
Solution Approach 2:
The system implements universal interfaces and communication protocols that allow AI agents and RPA robots to interact through standardized methods regardless of their specific functions. This multi-functional approach enables any agent or robot to work with any other component in the system without requiring custom integration for each pair.
2Reliability
If AI agents have human-in-the-loop capabilities to handle queries they cannot address, then decision accuracy improves, but response time and operational efficiency decrease due to additional human intervention
Solution Approach 1:
The system applies partial human intervention by implementing a tiered escalation mechanism where only queries that exceed the AI agent's confidence threshold or capability limits are escalated to human operators. Most routine queries are handled autonomously by the AI agent, while human-in-the-loop is activated selectively for complex or uncertain cases, balancing accuracy with response time.
Solution Approach 2:
The system implements feedback loops where human responses to escalated queries are captured, analyzed, and used to retrain and improve the AI agent's decision-making capabilities. This continuous learning process gradually reduces the number of queries requiring human intervention, improving both accuracy and response time over time.
3Extent of automation
If a unified system integrates multiple AI agents and RPA robots with self-healing capabilities, then autonomy and efficiency increase, but system complexity and difficulty of management increase
Solution Approach 1:
The unified system implements self-healing capabilities where AI agents and RPA robots can autonomously detect, diagnose, and resolve their own errors and failures without human intervention. The system includes automatic error recovery mechanisms, self-monitoring components, and adaptive learning that allow the system to maintain high autonomy while managing complexity through self-service operations.
Solution Approach 2:
The patent introduces a conductor application as an intermediary layer that manages the coordination, monitoring, and control of multiple AI agents and RPA robots. This mediator handles system-level complexity by providing centralized orchestration, resource management, and communication routing, allowing individual components to remain simple while the overall system achieves high autonomy and efficiency.
Data Source
AI summary
Unified solutions for agentic automation and robotic process automation (RPA) with self-healing and capabilities to increase autonomy are disclosed. AI agents “coexist” in tandem with RPA robots that execute RPA automations. The AI agents can dynamically leverage the tools available via these RPA robots to perform document processing, user interface (UI) automation, semantic copy-and-paste between a source and a target, etc. AI agents can dynamically select these tools and execute them in the form of a pipeline.