AI Integration Scenario Generation for Executable API Workflows
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Solution Overview
Problem
Generating executable integration scenarios is a tedious task prone to errors due to varying system architectures, technologies, and data formats, often involving repetitive tasks and inefficient selection of scenario steps, connectors, and adapters.
Innovation Solution
Utilizing generative artificial intelligence (Gen AI) to automatically generate and optimize data sequence integration scenarios by validating requests, determining intent, and providing a graphical representation of system and API interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual generation of integration scenarios is performed, then flexibility in scenario design is maintained, but productivity is low and errors are frequent
Solution Approach 1:
The patent replaces manual mechanical creation of integration scenarios with an AI-based automated system. The generative AI model processes natural language requests and automatically generates integration scenarios, replacing the manual task of creating scenarios step-by-step across multiple systems with an automated intelligent system.
Solution Approach 2:
The system enables self-service scenario generation where users can directly request integration scenarios through natural language without requiring manual configuration of complex system connections. The AI system autonomously analyzes requirements, identifies relevant systems and APIs, and generates appropriate integration scenarios automatically.
2Loss of time
If repetitive tasks are performed manually, then control over each step is maintained, but time consumption increases
Solution Approach 1:
The patent implements feedback mechanisms where the AI system validates generated scenarios against system capabilities and constraints. The system iteratively refines scenarios based on feedback from system validation, ensuring both time efficiency and execution reliability by catching errors during the generation process rather than during manual assembly.
Solution Approach 2:
The system performs preliminary validation and optimization of integration scenarios during the generation phase rather than during execution. By pre-validating scenario steps, system compatibility, and data format requirements, the system reduces time consumption while ensuring reliability before the scenario is executed.
3Adaptability or versatility
If multiple connectors and adapters are added manually, then system integration capability is enhanced, but complexity and error rates increase
Solution Approach 1:
The patent employs a universal AI system that handles multiple integration tasks through a single unified model. The generative AI model can generate various types of connectors and adapters for different system combinations without requiring separate specialized tools, thereby enhancing integration capability while reducing overall system complexity through multi-functionality.
Solution Approach 2:
The system dynamically adjusts connector and adapter configurations based on the specific system requirements and data formats involved. By changing parameters such as data formats, communication protocols, and system interfaces according to the generation request, the system achieves versatile integration capability without manually configuring each connection detail.
Data Source
AI summary
The disclosure generally describes methods, software, and systems for generation of a configurable and executable integration scenario. A request to generate a data sequence integration scenario is received. The request includes one or more textual requirements. The request is validated by processing the one or more textual requirements to determine inclusion of a minimal number of systems and actions. An intent and a context of the request are determined, using a first prediction engine, from the one or more textual requirements. The intent includes top-ranked systems and APIs matching the request. The intent and the context of the request are inputted as a prompt to a second prediction engine. The data sequence integration scenario is received, from the second prediction engine, responsive to the prompt. The data sequence integration scenario defines an order of the actions to be performed by the top-ranked systems and APIs matching the request.