AI Copilot for API Call Sequencing From Runtime Behavior

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Solution Overview

Problem

Existing AI-based copilots for software development focus solely on static code analysis and fail to consider the dynamic runtime behavior of API calls, leading to increased development time and error-prone manual sequencing of API endpoint calls.

Innovation Solution

An AI copilot that learns from both static code patterns and dynamic runtime behavior by monitoring and analyzing API interactions, providing real-time, context-aware suggestions for API endpoint sequences and generating code snippets.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If developers manually analyze API documentation and sequence API endpoint calls, then they can implement complex workflows, but development time and effort increase significantly

Engineering Contradiction:

Improvecorrect API call sequencingVSAvoiddevelopment time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables self-service by automatically analyzing API documentation and generating executable API call sequences without requiring manual developer intervention. The AI copilot autonomously processes documentation, identifies endpoint relationships, and produces ready-to-use code snippets that developers can directly integrate into their applications.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary action by pre-analyzing API documentation and pre-generating optimal API call sequences before the developer needs them. The AI copilot prepares comprehensive workflow recommendations in advance, including proper sequencing of endpoint calls and necessary data transformations, so developers receive ready-made solutions rather than starting from scratch.

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If developers experiment with API endpoints to understand workflows, then they can discover functionality, but errors increase and development becomes error-prone

Engineering Contradiction:

ImproveAPI functionality discoveryVSAvoidimplementation accuracy

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system implements feedback by continuously monitoring API documentation analysis results and comparing generated sequences against documented workflows. The AI copilot learns from feedback about what works and what doesn't, refining its understanding of API patterns and improving the accuracy of its recommendations over time, thereby reducing errors while maintaining adaptability.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The AI copilot acts as an intermediary between the API documentation and the developer, translating complex documentation into reliable, tested API call sequences. Rather than letting developers directly experiment with endpoints, the copilot mediates by providing pre-validated sequences that have been analyzed against the official documentation, reducing errors while preserving functionality discovery.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If AI copilots use static code analysis, then they can recognize code patterns, but they fail to understand dynamic runtime behavior of API calls

Engineering Contradiction:

Improvecode pattern recognitionVSAvoidruntime behavior information

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The system applies dynamics by transitioning from static code analysis to dynamic runtime behavior analysis. The AI copilot monitors actual API calls executed during runtime, observing the real sequences of endpoint calls and data transformations. This dynamic approach captures the actual behavior of APIs as they execute, providing insights that static analysis cannot obtain, while maintaining precise pattern recognition through learned runtime patterns.

Inventive Principle:

Principle #15Dynamics

4Loss of information

If comprehensive API documentation is provided, then developers can understand workflows, but documentation is often incomplete or non-standard

Engineering Contradiction:

Improveworkflow informationVSAvoiddocumentation quality

Core Design Contradiction:

Loss of informationVSEase of manufacture

Solution Approach 1:

The system applies parameter changes by transforming incomplete or non-standard documentation into structured, standardized workflow representations. The AI copilot analyzes the available documentation regardless of its quality, extracts meaningful patterns, and reconstructs complete workflow information by inferring missing details and standardizing inconsistent formats, thereby recovering lost information while overcoming poor documentation quality.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentEP4703876A1Computer-implemented method to support software developers in-development environment to encode an application using external application programming interfaces and application programming interfaces, api, server

Publication Date: 2026.03.04 SIEMENS AG

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AI summary

An application programming interface (API) server comprising an API gateway and at least one API comprising a plurality of callable API endpoints, and a method thereto, wherein the server further comprises: an API monitor configured to retrieve API call data relating to API calls made by a client to one or more API endpoints of one or more APIs, to timestamp each API call in the retrieved API call data, and to send the timestamped API call data to a data storage system; the data storage system configured to store the received timestamped API call data;an artificial intelligence, AI, copilot that is configured to be trained using the data stored in the data store and can be used in a development environment to make suggestions to the user for creating code for a new application.