Agentic-RPA Orchestration with Conductor Layers for Self-Healing
Solution Overview
Problem
Current AI agents employed in software automation lack effective interoperability with other applications and software functionality, operating independently and not effectively integrating with RPA robots.
Innovation Solution
Unified solutions for agentic automation and RPA with self-healing capabilities, where AI agents and RPA robots work together to execute tasks, dynamically selecting tools and making decisions, and agentic orchestration manages their execution, allowing for increased autonomy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI agents operate as independent software applications, then they maintain independence and simplicity in development, but they lack effective interoperability with other applications and software functionality
Solution Approach 1:
The system is segmented into distinct components: conductor applications for orchestration, AOP engines for process management, AI agents for intelligent tasks, and RPA robots for automation tasks. Each component operates independently but communicates through standardized interfaces, enabling interoperability without increasing individual component complexity
Solution Approach 2:
The conductor application serves as an intermediary that manages communication and coordination between AI agents, RPA robots, and other system components. The AOP engine acts as another intermediary layer that orchestrates automation processes, enabling seamless interoperability while maintaining simplicity of individual components
2Productivity
If AI agents and RPA robots execute automations independently, then they maintain operational simplicity, but they cannot effectively collaborate on complex automation tasks
Solution Approach 1:
The system merges AI agents and RPA robots into a unified automation ecosystem where they can collaborate on complex tasks. The conductor application and AOP engine provide the orchestration framework that enables this merging, allowing intelligent decision-making and rule-based automation to work together seamlessly
Solution Approach 2:
The conductor application and AOP engine serve multiple functions: they orchestrate AI agents and RPA robots, manage automation workflows, handle task distribution, and coordinate execution results. This multi-functionality enables effective collaboration without requiring separate specialized systems for each function
3Productivity
If human intervention is required for AI agent decisions, then accuracy and control are improved, but productivity and automation extent are reduced
Solution Approach 1:
The system applies partial automation where AI agents handle routine decisions autonomously to maintain high productivity, while human intervention is selectively applied to complex or high-stakes decisions to ensure accuracy. The conductor application manages this balance by routing tasks appropriately
Solution Approach 2:
The system implements feedback loops where AI agents learn from human corrections and decisions. The conductor application and AOP engine track automation outcomes and use this feedback to improve future AI agent performance, gradually reducing the need for human intervention while maintaining or improving decision accuracy
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.