AI Workflow Delegation With Human-in-the-Loop Task Handoffs
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Solution Overview
Problem
Conventional electronic devices with business application software operate in a reactive mode, relying on human agents to initiate tasks, which is inefficient and costly, and intelligent agents face barriers in leveraging machine-learning models for complex tasks due to lack of trust, data access, and practical integration with human experts.
Innovation Solution
A system that integrates AI agents to autonomously manage workflows, dynamically seek human assistance when needed, and learn from expert feedback, using advanced machine learning models to enhance task performance and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human agents manually initiate and execute tasks on electronic devices, then task execution can be completed with simple system architecture, but productivity is limited by human speed and resource consumption increases
Solution Approach 1:
The system enables self-service by allowing AI agents to autonomously initiate, execute, and manage tasks without requiring human agents to manually start or navigate through application interfaces. The electronic device with AI agent automatically detects task requirements, launches appropriate applications, and executes tasks based on workflow definitions, eliminating the need for human intervention in routine task initiation.
Solution Approach 2:
The patent replaces the mechanical human-operated system with an automated AI agent system. Instead of human agents physically interacting with device interfaces to initiate tasks, the AI agent uses automated mechanisms to detect task needs, select appropriate applications, and execute tasks through programmatic interfaces, thereby increasing speed and reducing resource consumption.
2Productivity
If AI agents autonomously execute tasks using machine-learning models, then productivity and task execution speed increase, but reliability decreases due to lack of human validation and trust
Solution Approach 1:
The system introduces human agents as intermediaries in the task execution workflow. When AI agents execute tasks, human agents can review, validate, and approve outcomes before final execution or correction. This intermediary layer maintains productivity by allowing AI to handle most tasks autonomously while providing human validation for critical decisions, thereby building trust without significantly reducing speed.
Solution Approach 2:
The system implements feedback loops where AI agents receive validation and correction from human agents on task outcomes. This feedback mechanism allows the AI to learn from human expertise, improve decision-making accuracy over time, and maintain reliability while preserving the speed advantages of automated execution. Human feedback serves as a training signal for continuous improvement.
3Ease of operation
If complex workflows require human agent intervention for each task initiation, then ease of operation is maintained with simple interfaces, but loss of time increases due to serial human execution
Solution Approach 1:
The system performs preliminary actions by pre-defining workflow sequences and task dependencies before execution begins. The AI agent automatically detects when tasks need to be initiated, selects appropriate applications in advance, and prepares execution parameters beforehand, eliminating the need for human agents to manually navigate interfaces during task execution and significantly reducing completion time.
Solution Approach 2:
The patent enables continuous task execution by allowing the AI agent to autonomously manage workflow sequences without human interruption. Once a workflow is initiated, the AI agent continuously executes tasks in sequence, manages transitions between applications, and handles intermediate steps without requiring human agents to re-engage the interface, thereby maintaining ease of operation while eliminating time losses associated with human serial execution.
4Productivity
If intelligent agents are deployed to perform complex tasks, then productivity increases, but device complexity increases requiring advanced machine-learning models and data infrastructure
Solution Approach 1:
The system segments the intelligent agent functionality into modular components: workflow definition module, task detection module, application selection module, and execution module. Each component has a specific function and can be independently developed and deployed. This segmentation reduces device complexity by allowing the system to build sophisticated task execution capabilities through composition of simpler, well-defined modules rather than requiring a monolithic complex system.
Data Source
AI summary
Provided is a process, including: executing, with the computer system, tasks in the workflow with AI agents; detecting, with the computer system, that a human is needed to assist with a given task among the tasks in the workflow after one of the AI agents attempts the given task; selecting, with the computer system, the human from among a set of humans based on a profile of the human and the given task and sending a message that causes the selected human to perform the given task; and receiving, with the computer system, the selected human's completion of the given task and inputting the human completion of the given task, or input, to one of the AI agents performing a downstream task in the workflow; and storing, with the computer system, a result of completing the workflow in memory.