LLM Task Agent Iteration for Multi-Source Query Orchestration
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Solution Overview
Problem
Conventional machine learning models struggle to dynamically select appropriate applications for generating reports due to dynamic data and configuration challenges, often requiring human intervention or complex rules, and are inadequate for handling requests involving data retrieval and mathematical operations across multiple data sources.
Innovation Solution
A task agent, utilizing a machine learning language processing model, automatically selects and manages applications to iteratively retrieve data from multiple data sources, performing necessary operations to generate comprehensive results by iteratively executing applications and generating schema information for data queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional machine learning models use a predetermined sequence of applications to generate a single data query from a single data source, then the system configuration is simple, but the system cannot dynamically select appropriate applications and cannot adequately respond to complex information requests involving multiple data sources
Solution Approach 1:
The system transitions from a static predetermined sequence of applications to a dynamic selection mechanism where the machine learning model automatically determines which applications to use based on the specific information request. The model can adaptively select applications from the plurality of available applications, enabling dynamic configuration that responds to the unique requirements of each query while managing complexity through automated decision-making.
Solution Approach 2:
The machine learning model performs self-service by autonomously selecting appropriate applications and constructing data queries without human intervention. The model evaluates the information request and independently determines the optimal application sequence and data sources, eliminating the need for manual configuration or complex rule-based systems while maintaining adaptability across diverse query types.
2Extent of automation
If conventional systems rely on human input or complicated rules to select applications, then application selection can be accurate, but the system requires manual intervention and has low automation
Solution Approach 1:
The system replaces the mechanical approach of human selection or rule-based automation with an intelligent machine learning model. The model processes the information request and automatically selects appropriate applications based on learned patterns and relationships, achieving both high automation and maintained reliability through the model's ability to understand query intent and match it with suitable applications.
Solution Approach 2:
The system changes the parameter of decision-making from fixed rules or human judgment to a flexible machine learning model that can adjust its selection criteria based on the specific characteristics of each information request. This allows the system to maintain accuracy by adapting to different query types while achieving full automation through the model's autonomous decision-making capability.
3Measurement precision
If the machine learning model generates a single data query without iterative processing, then the processing speed is fast, but the results may not be accurate or comprehensive enough to adequately respond to the request
Solution Approach 1:
The system implements periodic action through iterative processing where the machine learning model generates data queries in successive iterations. After each query execution, the model evaluates whether the results adequately respond to the information request. If not, the model generates additional queries or modifies existing ones, continuing this periodic cycle until satisfactory results are achieved, thus ensuring accuracy and completeness while managing time through structured iteration.
Solution Approach 2:
The system incorporates feedback mechanisms where the results of each data query are evaluated by the machine learning model to determine if they adequately respond to the information request. This feedback loop allows the model to adjust its approach, generate additional queries if needed, or refine existing queries to improve result accuracy and completeness, balancing the need for comprehensive results with efficient time utilization through intelligent feedback-driven iteration.
Data Source
AI summary
A computer using the systems and methods described herein can use a task agent to automatically select and manage different applications and agents to use to generate results in response to requests for information. The computer can iteratively execute the task agent to generate the results. For instance, the task agent can identify a set of applications to use to generate a result in response to an information request. The task agent can identify the set of applications based on an intent of the information request. The task agent can iteratively execute different sequences of applications from the set of applications to query and retrieve data from different data sources until determining the data that is necessary to generate a response to the request has been retrieved. The computer can generate the response from the retrieved data and present the response on a user interface.