Multi-Agent Search Client for Natural-Language Video Conferencing Content
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Solution Overview
Problem
Existing video conferencing systems face inefficiencies in managing and searching large volumes of content due to inflexible traditional search methods that do not support natural language queries and lack the ability to perform arbitrary operations, particularly in multi-member organizations.
Innovation Solution
Implementing a multi-agent search client with a search agent application that utilizes a language model to divide natural language queries into portions, assigning them to specialized domain agents, which then execute steps using executors to generate responses, allowing for natural language interactions and arbitrary searches within video conferencing platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional search methods are used in video conferencing systems, then system simplicity is maintained, but search flexibility and natural language support are insufficient
Solution Approach 1:
The system segments the search functionality into multiple specialized components: a search agent that handles natural language processing, domain agents that execute specific search operations, and executors that perform concrete actions. This segmentation allows each component to be optimized for its specific function while maintaining overall system flexibility without excessive complexity.
Solution Approach 2:
The search agent acts as an intermediary between the user's natural language query and the domain agents. It translates human-readable queries into structured instructions that can be executed by the specialized agents, thereby enabling natural language support without requiring the entire system to be rewritten.
2Adaptability or versatility
If traditional search methods are used, then processing resources are conserved, but the ability to perform arbitrary operations and natural language queries is limited
Solution Approach 1:
The system dynamically allocates processing resources based on the specific query being executed. The search agent analyzes each query and routes it to appropriate domain agents only when needed, rather than maintaining constant processing power. This dynamic approach enables arbitrary operations and natural language queries while conserving resources during idle or simple operations.
3Measurement precision
If multiple specialized models are used for different search operations, then search precision is improved, but model complexity and resource requirements increase
Solution Approach 1:
The domain agents are designed with multi-functionality, capable of executing various types of search operations across different domains (video conferencing, chat, documents, meetings, user information). This universal design allows a single agent framework to handle diverse search tasks with high precision without requiring separate specialized models for each operation, thereby reducing overall model complexity.
4Adaptability or versatility
If complex multi-agent systems are implemented, then search functionality is enhanced, but ease of operation decreases
Solution Approach 1:
The search agent serves as an intelligent intermediary that shields users from the complexity of the multi-agent system. Users simply provide natural language queries without needing to understand or configure the underlying agents and executors. The search agent automatically translates user intent into appropriate agent instructions and interprets results, maintaining ease of operation while enabling enhanced search functionality.
Data Source
AI summary
Techniques are disclosed relating to a multi-agent search client. In an example method, a search agent application receives a query. The search agent application determines, using a search agent, one or more agents to each execute a portion of the query. The search agent application outputs, to a domain agent, a portion of the query. The search agent application determines, using the domain agent, an execution step based on the portion of the query and information about an executor. The executor generates instructions based on the execution step that are configured to generate a response to the execution step and outputs a command to execute the instructions. The executor then outputs, to the first domain agent, the response to the execution step. The search agent application outputs, using the search agent, a response to the query based on the response to the execution step.