ML Agent Enterprise Integration Script Generation
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Solution Overview
Problem
Traditional methods for enterprise systems integration are labor-intensive, time-consuming, and lack flexibility, particularly when dealing with diverse and rapidly changing systems, and struggle with incorporating unstructured data from legacy systems that are not compliant with modern integration standards, leading to information silos and inefficient data retrieval.
Innovation Solution
The use of specialized machine learning models, such as Large Language Models (LLMs), to dynamically generate scripts for retrieving information from various enterprise systems, including legacy systems, and to reconfigure user interfaces based on data context and user instructions, enabling a more flexible, reliable, and scalable integration process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional Enterprise Service Bus (ESB) architecture is used for system integration, then system integration can be achieved, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces traditional mechanical ESB integration architecture with a machine learning-based system that automatically generates integration scripts. The ML model analyzes system schemas and dynamically creates integration code, eliminating manual coding efforts and significantly reducing integration development time while maintaining system integration capabilities.
Solution Approach 2:
The integration system performs self-service by automatically generating integration scripts without requiring manual programmer intervention. The ML model autonomously analyzes source and target system schemas, determines appropriate integration approaches, and produces ready-to-use integration code, making the system self-configuring and self-adapting.
2Adaptability or versatility
If traditional integration methods are used, then existing systems can be integrated, but flexibility to adapt to changing requirements is limited
Solution Approach 1:
The patent implements dynamic integration by using ML models that can adapt to changing system requirements in real-time. The system continuously learns from new data sources and integration patterns, dynamically generating updated integration scripts without requiring architectural redesign, thus providing high flexibility while maintaining stable core integration functionality.
Solution Approach 2:
The integration system changes parameters dynamically by adjusting integration strategies, data transformation rules, and script generation parameters based on the specific characteristics of source and target systems. The ML model modifies integration parameters automatically to optimize performance for different system combinations and evolving business requirements.
3Ease of operation
If manual coding is used for user interfaces, then functional interfaces can be created, but development resources and maintenance costs increase
Solution Approach 1:
The patent replaces manual UI coding with an ML-based automatic interface generation system. The model analyzes data schemas and integration logic to automatically generate user interface code, eliminating the need for manual UI development while maintaining full functionality. This significantly reduces development complexity and resource requirements.
Solution Approach 2:
The ML-based interface generation system provides universal functionality by creating adaptable user interfaces that work across multiple data sources and integration scenarios. The generated interfaces are not hardcoded for specific functions but can dynamically adapt to various data types and business requirements, reducing the need for multiple specialized interface implementations.
4Adaptability or versatility
If statically coded user interfaces are used, then interfaces can be displayed, but dynamic adaptability to evolving business needs is lost
Solution Approach 1:
The patent implements dynamic user interfaces generated by ML models that automatically adapt to evolving business needs. The system continuously monitors data source changes and business requirements, then dynamically regensates interface code to reflect new requirements, providing real-time adaptability without manual intervention and maintaining high update efficiency.
Data Source
AI summary
A method for directing a domain-specific request to a machine learning (ML) agent of a plurality of ML agents configured to generate domain specific language (DSL) scripts includes, at a computing system: processing a request received from a user to determine a particular domain associated with the request; identifying an ML agent of the plurality of ML agents that is associated with the particular domain; generating, using the ML agent, a DSL script for the particular domain based on the request; executing, using the ML agent, at least one of an API call and a database query based on the DSL script; processing, using the ML agent, a response to the at least one of the API call and the database query; generating, using the ML agent, an output based on the response; and processing the output using at least one other ML agent of the plurality of ML agents.