Tool-Based AI Data Management Across Diverse Source Systems

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Solution Overview

Problem

Existing data management solutions are time-consuming to configure and use for new tasks involving diverse data source systems, lacking the ability to efficiently interact and manage data across multiple systems.

Innovation Solution

An AI agent trained with a machine learning model is used to generate execution plans for tasks, invoking specific tools to interact with data source systems based on user permissions, leveraging role-based access privileges to autonomously or semi-autonomously perform actions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional data management solutions are used to interact with diverse data source systems, then data can be accessed and managed, but the configuration and task execution become time-consuming and inefficient

Engineering Contradiction:

Improvedata management efficiencyVSAvoidconfiguration time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The AI agent autonomously performs data management tasks by interpreting natural language queries and executing appropriate actions on diverse data source systems without requiring manual configuration or intervention, thereby significantly reducing configuration time and improving productivity

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The AI agent serves as an intermediary between the user and multiple diverse data source systems, translating user intent into system-specific actions and handling the complexity of interfacing with different systems, which eliminates the need for users to manually configure each system connection

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If existing data management tools are used to accomplish new tasks across multiple data source systems, then data access is possible, but the process lacks intelligence and requires significant manual effort

Engineering Contradiction:

Improvecapability to accomplish new tasksVSAvoiduser effort required

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The AI agent autonomously analyzes user queries, determines the appropriate data source systems and actions required, and executes tasks independently, transforming manual operations into self-service automation that reduces user effort while maintaining high adaptability to new tasks

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system changes the operational parameters from manual command execution to AI-driven autonomous task completion, where the AI agent dynamically adjusts its behavior based on query complexity and system requirements, enabling easy operation across diverse tasks

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250335443A1Artificial intelligence-assisted data management for diverse source systems

Publication Date: 2025.10.30 COHESITY INC

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.