Multi-Agent AI Task Orchestration for Relevant Content Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Integrating generative AI models into productivity applications complicates user interfaces and workflows due to the risk of generating large amounts of irrelevant or low-quality content lacking sufficient context for the task at hand, leading to increased user effort in sifting through unnecessary information.
Innovation Solution
A multi-agent system guided by generative AI models, where an orchestration layer coordinates multiple agents to manage tasks, including task management agents and execution agents, to ensure relevant content generation by populating prompts with task-specific contextual information and evaluating the output for relevance and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative AI models are integrated into productivity applications to automate content generation, then productivity is improved, but the user interface and workflow become more complex
Solution Approach 1:
The patent introduces an intermediary layer between the user and the generative AI model. This intermediary manages the complexity of prompt construction, context retrieval, and model interaction, presenting a simplified interface to users while handling the computational complexity in the background.
Solution Approach 2:
The system segments the content generation process into multiple components: context retrieval module, prompt construction module, model invocation module, and output processing module. This segmentation allows each component to be optimized independently and simplifies the overall system architecture.
2Productivity
If generative AI models generate large amounts of content rapidly, then content volume increases, but the relevance and quality of content decreases
Solution Approach 1:
The system performs preliminary actions by retrieving and organizing relevant context information before generating content. This preliminary context gathering ensures that the generative model has sufficient and accurate information to produce high-quality, relevant content from the first generation.
Solution Approach 2:
The system implements feedback mechanisms where generated content is evaluated against task requirements and user preferences. This feedback loop allows the system to iteratively refine prompts and adjust generation parameters to improve content quality and relevance.
3Quantity of substance
If users sift through irrelevant or low-quality content to find useful content, then content volume increases, but the time required for content review increases
Solution Approach 1:
The system extracts and removes irrelevant content from the generation process by filtering prompts and context information to ensure only relevant data is used for content generation. This extraction of relevant information upfront prevents time-consuming review of irrelevant content.
Solution Approach 2:
The system performs preliminary sorting and organization of generated content based on relevance criteria before presenting it to users. This preliminary action reduces the time users need to spend reviewing content by pre-filtering for relevance.
Data Source
AI summary
Systems, methods, and software are disclosed herein for a system of agents for managing tasks of software applications which is guided by generative AI. In an implementation, a computing apparatus determines that a task has been assigned to an application assistant of an application. The application assistant includes multiple agents which interact with a generative AI model. The computing apparatus orchestrates the multiple agents in their interactions with the generative AI model in furtherance of completing the task and updates the contextual information of the task based on the interactions.