LLM Task Orchestration With Specialized Software Agents
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for managing organizational tasks are time-consuming, error-prone, and expensive, and may introduce security concerns, particularly in handling customer information.
Innovation Solution
Utilizing large language models (LLMs) and software agents to automate task management by identifying tasks from data sets, such as meeting transcripts or customer interactions, and executing these tasks with customized software agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to track tasks and updates, then representatives can maintain control over project details, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system enables self-service automation where software agents independently execute tasks based on meeting transcripts without requiring manual intervention. The LLM automatically processes meeting content, identifies tasks, assigns them to appropriate agents, and tracks completion, allowing the system to serve itself rather than requiring representative management
Solution Approach 2:
The patent replaces the mechanical manual tracking system with an automated computational system. Instead of representatives manually recording and tracking tasks, the system uses LLMs to process meeting transcripts and software agents to execute tasks automatically, substituting human mechanical operations with automated digital processes
2Loss of information
If multiple representatives manually track tasks, then task visibility is maintained, but resources are wasted and costs increase
Solution Approach 1:
The system implements a universal task management approach where a single automated system performs multiple functions: processing meeting transcripts, identifying tasks, assigning tasks to appropriate agents, tracking task status, and notifying relevant parties. This multi-functional system replaces the need for multiple representatives to perform separate tracking functions
Solution Approach 2:
The patent introduces software agents as intermediaries between the meeting transcript and task execution. These agents act as mediators that receive task assignments from the LLM, execute the appropriate actions, and report back on completion, thereby maintaining complete task tracking information while eliminating the need for multiple human representatives
3Adaptability or versatility
If manual task management is used, then flexibility in handling various task types is maintained, but security concerns arise with customer information
Solution Approach 1:
The system segments task management into distinct software agents, each specialized for specific task types. This segmentation allows the system to handle various task types flexibly while maintaining security, as each agent operates within its defined scope and can be granted appropriate access permissions only when needed, rather than giving broad access to multiple human representatives
Data Source
AI summary
A computing system may be used to support techniques to automate tasks using large language models (LLMs) and software agents. A user device may provide a data set to the computing system, and the computing system may process the data set to identify the one or more tasks using an LLM. The computing system may select one or more software agents to execute each of the identified tasks. For example, the computing system may identify a respective type for each task, and the computing system may select a respective software agent of a set of supported software agents configured to execute the respective type of task. Each software agent may execute a respective task to produce an output, such as by generating a summary of a transcript, transmitting one or more communications to users associated with the organization, or providing responses to inquiries, among other examples.