AI-Generated Application Logic for Adaptive Enterprise Software
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Enterprise software often faces challenges with inflexible and brittle business logic layers that are slow to adapt to new needs, leading to difficulties in updating, maintenance, and scalability issues.
Innovation Solution
A dynamic AI-driven system that generates business logic from user inputs via graphical user interface or chat interfaces, using intents, corpora, and transforms to process requests in real time, reducing dependence on hardcoded logic and enhancing adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hardcoded business logic is used in enterprise software, then the system structure is simple and easy to implement, but the system becomes inflexible and slow to adapt to new requirements
Solution Approach 1:
The patent implements dynamic business logic generation where the system transitions from static hardcoded logic to dynamically generated logic that adapts to changing requirements. The AI agent generates execution plans and code on-demand based on user queries, allowing the system to evolve without manual reconfiguration while maintaining manageable complexity through automated generation.
Solution Approach 2:
The patent introduces an AI agent as an intermediary layer between user requirements and system execution. This intermediary automatically generates business logic, execution plans, and code based on natural language queries, eliminating the need for direct hardcoded logic while maintaining system coherence and reducing the complexity burden through intelligent mediation.
2Productivity
If traditional business logic layers are used, then implementation is straightforward, but updating and maintenance require significant developer time and resources
Solution Approach 1:
The patent enables self-service business logic generation where the AI agent autonomously creates, updates, and maintains execution plans and code based on user queries. This self-service capability eliminates the need for manual developer intervention in routine updates, dramatically increasing update speed while reducing maintenance time through automated self-modification of the business logic layer.
Solution Approach 2:
The patent implements preliminary action by having the AI agent generate and validate execution plans before actual system execution. The system pre-processes requirements, generates appropriate business logic, and prepares execution plans in advance, allowing rapid updates without extensive maintenance intervention when requirements change.
3Adaptability or versatility
If hardcoded business logic is implemented, then the initial development is efficient, but the system lacks scalability as organizational size and complexity increase
Solution Approach 1:
The patent implements dynamic logic generation that scales with organizational needs. As requirements become more complex, the AI agent adapts by generating increasingly sophisticated execution plans and business logic automatically, allowing the system to scale without proportionally increasing manual development complexity. The dynamic generation approach handles complexity through intelligence rather than rigid structure.
Solution Approach 2:
The patent utilizes parameter changes by allowing the AI agent to adjust business logic parameters, data sources, and execution strategies based on changing organizational requirements. This parameter-based adaptability enables scalable growth where the same core system can handle varying levels of complexity by dynamically modifying logic parameters rather than requiring complete rearchitecture.
Data Source
AI summary
A computer-implemented method for enhancing application logic may include receiving, by an artificial intelligence (AI) agent, a query from a user; processing, by the AI agent, the query to derive an intent; building, by the AI agent, an execution plan based on the intent; retrieving, by the AI agent, in accordance with the execution plan, data from one or more data sources; processing, by the AI agent, the data via one or more transforms in accordance with the execution plan; and returning, by the AI agent, a result based on output produced by the one or more transforms. Various other methods, systems, and computer-readable media are also disclosed.