Multi-Agent Conversation Flow for Accurate Intent Refinement
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Solution Overview
Problem
Existing virtual agents struggle to accurately determine user intent when inputs lack known keywords or command phrases, often resulting in error messages or ignoring relevant information.
Innovation Solution
A multi-agent conversation system where multiple virtual agents are initialized based on candidate intents, each providing different responses, with the list of intents refined through subsequent user inputs, utilizing linguistic, biometric, and profile data to activate and deactivate agents accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a single virtual agent uses keyword matching to respond to user input, then response speed is fast, but accuracy in determining user intent deteriorates when keywords are absent
Solution Approach 1:
The system segments the single agent into multiple specialized agents, each trained to handle specific intent scenarios. This segmentation allows parallel processing of different intent possibilities, maintaining fast response while improving accuracy through specialized detection capabilities.
Solution Approach 2:
The multi-agent system creates a universal response mechanism where multiple agents with different expertise collectively handle various user intents. Each agent contributes its specialized knowledge, making the overall system more accurate across diverse scenarios while maintaining efficient parallel processing.
2Measurement precision
If pre-scripted responses are used to identify user intent, then intent recognition capability is improved, but the system ignores keywords and information in user input
Solution Approach 1:
The system implements feedback loops where each agent's response is evaluated against user reactions. This feedback mechanism allows the system to learn from interactions, continuously improving intent recognition while adapting to utilize keywords and information from user input more effectively.
Solution Approach 2:
Multiple agents prepare preliminary responses based on different intent interpretations before the user responds. This preliminary action allows the system to present multiple intent possibilities simultaneously, then refine based on actual user feedback, preserving valuable input information while improving intent detection.
3Measurement precision
If multiple agents are initialized for each candidate intent, then user intent recognition accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex multi-agent problem into manageable components: intent detection module, agent selection module, response generation module, and feedback processing module. This segmentation reduces overall system complexity while maintaining the benefits of multiple specialized agents.
Solution Approach 2:
The system merges multiple agents' responses through a centralized coordination mechanism that consolidates their outputs. This merging approach manages complexity by providing a unified interface while leveraging the collective intelligence of multiple specialized agents behind the scenes.
4Adaptability or versatility
If the list of candidate intents is expanded to cover more possibilities, then intent coverage is improved, but the number of agents to initialize increases beyond threshold
Solution Approach 1:
The system applies partial action by initializing only a subset of agents based on confidence thresholds and input analysis. Rather than activating all possible agents, it selectively engages those most relevant to the current input, maintaining intent coverage while controlling system complexity through intelligent filtering.
Data Source
AI summary
A first input is received from a user input device. Based on the first input, a list of candidate intents is generated, and a plurality of agents is initialized. Each agent of the plurality of agents corresponds to a respective candidate intent. Each agent then provides a different response to the first input in accordance with its respective corresponding intent. A second input is then received that responds to one or more of the agents. Based on the agents to which the second input is responsive, the list of candidate intents is refined and, based on the refined list, one or more agents are deactivated.