AI Task Management Agent for Dialogue-Guided Task Decomposition
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Solution Overview
Problem
Existing agent-type AI systems using generative AI struggle with controlling task execution propriety and situation through interactive dialogue, lack long-term storage of dialogue content, require specific and detailed user instructions, and often make unrealistic task determinations due to semantic similarity comparisons.
Innovation Solution
An agent system utilizing generative AI is configured with a dialogue unit, solution unit, execution unit, and monitoring unit, enabling autonomous task decomposition and execution, long-term storage of dialogue logs, and interactive task correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple highly specialized AI agents are provided for respective services, then service specialization and response accuracy are improved, but system complexity and difficulty in realizing comprehensive response increase
Solution Approach 1:
The system segments AI agents into specialized service agents (e.g., reservation agent, recommendation agent) and a centralized task management agent. Each service agent handles specific tasks with high accuracy, while the task management agent coordinates them, resolving the contradiction between specialization and system complexity.
Solution Approach 2:
A task management agent is introduced as an intermediary between the user and multiple specialized service agents. This mediator receives user instructions, decomposes them into subtasks, and assigns them to appropriate service agents, enabling comprehensive response without requiring the user to manage complex agent interactions directly.
2Manufacturing precision
If detailed user instructions are required for each task, then task execution precision is improved, but ease of operation and user burden deteriorate
Solution Approach 1:
The task management agent performs preliminary actions by automatically decomposing user instructions into detailed subtasks and assigning them to appropriate service agents. This eliminates the need for users to provide detailed instructions manually, improving ease of operation while maintaining task execution precision through automated task breakdown.
Solution Approach 2:
The system enables self-service by allowing the task management agent to autonomously analyze user instructions, determine required subtasks, and allocate them to suitable service agents without user intervention. This automated self-organization maintains precision while significantly improving ease of operation.
3Productivity
If abstract user instructions are autonomously decomposed into specific tasks, then productivity and ease of operation are improved, but reliability of task determination deteriorates
Solution Approach 1:
The task management agent implements feedback mechanisms by monitoring the decomposition and assignment process. It can review generated subtasks, verify their appropriateness, and adjust assignments based on service agent capabilities and current system state, thereby maintaining reliability while achieving high productivity through automated decomposition.
4Adaptability or versatility
If generative AI is used for autonomous task decomposition and execution, then adaptability and ease of operation are improved, but measurement precision of task determination worsens
Solution Approach 1:
The system segments the task determination process into multiple stages: understanding user intent, decomposing into subtasks, and assigning to service agents. Each stage is handled by specialized components that maintain precision while leveraging generative AI's adaptability for overall task decomposition.
Solution Approach 2:
The task management agent serves as an intermediary that combines generative AI's adaptability in understanding diverse user instructions with rule-based precision in task decomposition and assignment. This mediator ensures that while the system is highly adaptable to different user needs, the actual task determination maintains precision through structured processing.
Data Source
AI summary
The dialogue unit 11 grasps a business instruction by a dialogue with a user 2 by the generative AI, and stores a content of the dialogue as a dialogue log in a short-term storage 16, the solution unit 12 creates a task list by decomposing the business instruction into tasks by the generative AI and passes an execution instruction of each task to the execution unit 13, the execution unit 13 executes a work on a corresponding data source 3 by the generative AI corresponding to the task related to the execution instruction, and passes an execution result to the solution unit 12, and the monitoring unit 14 refers to the dialogue log at any time, grasps a context of the dialogue by the generative AI, predicts a content to be dealt with next, stores the content as a summary in the short-term storage 16.