AI Agent Tool Orchestration Across Diverse Data Sources
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Solution Overview
Problem
Existing data management solutions are time-consuming and inefficient when interacting with diverse data source systems, requiring manual integration and lacking seamless coordination across multiple systems, which hampers user capabilities and response times.
Innovation Solution
An AI agent trained to interact with users and leverage tools to autonomously or semi-autonomously perform tasks across diverse data source systems, utilizing role-based access privileges to ensure secure and efficient data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual integration methods are used to interact with diverse data source systems, then system complexity and configuration time increase, but existing solutions can accomplish basic data access tasks
Solution Approach 1:
The AI agent autonomously performs data management tasks across diverse systems without requiring manual configuration or intervention. It self-services by independently interpreting user intent, selecting appropriate tools, executing actions, and coordinating across multiple data source systems, thereby eliminating time-consuming manual setup while maintaining high productivity
Solution Approach 2:
The AI agent acts as an intermediary between users and diverse data source systems, translating natural language queries into system-specific actions. This intermediary layer handles the complexity of integrating multiple protocols and formats, enabling fast task completion without exposing users to underlying system complexity or configuration requirements
2Ease of operation
If existing data management solutions are used to coordinate actions across multiple data source systems, then manual integration is required, but basic data access can be achieved
Solution Approach 1:
The AI agent autonomously manages the complexity of coordinating actions across multiple data source systems. It independently selects appropriate tools for each system, formulates correct protocol-specific requests, and executes actions without requiring users to understand or configure integration details, thereby enhancing ease of operation while handling integration complexity internally
Solution Approach 2:
The AI agent serves as an intermediary that abstracts away the complexity of multiple data source systems. It translates high-level user intent into system-specific operations, managing protocol differences and integration complexity behind the scenes while presenting a simplified interface to users, thus improving ease of operation without sacrificing coordination capability
3Speed
If sequential processing is used to interact with data source systems, then response time increases, but system reliability is maintained
Solution Approach 1:
The AI agent dynamically adjusts its processing strategy based on task requirements and system responses. It can execute actions sequentially when dependencies exist or switch to concurrent execution when actions are independent, optimizing response time while maintaining reliability through adaptive coordination of multiple data source system interactions
Solution Approach 2:
The AI agent performs preliminary analysis of the execution plan to identify actions that can be executed concurrently. By pre-planning and initiating independent actions simultaneously rather than sequentially, it reduces overall processing time while maintaining system reliability through proper coordination of results
Data Source
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AI summary
In an example, a method comprises generating, with a computing system-executed AI agent applying a machine learning model, based on a query associated with a user, an execution plan for a task to satisfy the query, wherein the execution plan includes actions to be performed with respect to a first data source system and a second data source system, and wherein the user has permission for each of the actions; invoking, by the AI agent, a first tool to perform a first action of the actions with respect to the first data source system, wherein the AI agent is trained to use the first tool; and invoking, by the AI agent, a second tool to perform a second action of the actions with respect to the second data source system, wherein the AI agent is trained to use the second tool.