Agent-Based Software Development for Semantic Task Assignment
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Solution Overview
Problem
Existing methods and tools for code-based modular software development lack an integral and scalable solution for demand analysis, task assignment, and code integration, often failing to capture the semantics and intention of natural language demands due to ambiguity, incompleteness, and inconsistency.
Innovation Solution
A method using a language model for determining quantitative representations of demands, clustering them into tasks, and assigning these tasks to agents based on agent expertise, with mechanisms for semantic analysis, scoring functions for communication protocol selection, and continuous integration/deployment tools for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If NLP technology is used to extract and represent information from natural language demands, then the processing speed and automation level are improved, but the accuracy and completeness of capturing semantics and intention deteriorate due to ambiguity, incompleteness, and inconsistency of natural language
Solution Approach 1:
The patent introduces an intermediary representation layer (structured data format with explicit fields for semantics, intention, requirements, and constraints) between raw natural language and the software development system. This intermediary structure mediates the translation process, allowing NLP to extract information while maintaining explicit representations of semantic meaning and intent, thus resolving the contradiction between automation and accuracy.
2Productivity
If multiple agents are used for code-based modular software development, then the productivity and scalability are improved, but the complexity of coordination and assignment among agents increases
Solution Approach 1:
The patent segments the software development process into distinct, well-defined tasks with explicit inputs, outputs, and constraints. Each agent is assigned specific task types based on their expertise, and the structured demand representation divides requirements into manageable components. This segmentation reduces coordination complexity by creating clear boundaries and interfaces between agents while maintaining high productivity through specialized分工.
3Ease of operation
If natural language demands are processed without structured representation, then the ease of operation and flexibility are improved, but the reliability and consistency of task assignment deteriorate due to ambiguity and incompleteness
Solution Approach 1:
The patent performs preliminary action by automatically structuring and validating natural language demands before task assignment occurs. The system pre-processes demands into a standardized format with explicit fields for semantics, intention, requirements, and constraints, and validates completeness against predefined criteria. This preliminary structuring ensures reliability of subsequent task assignment while maintaining ease of operation, as users can still input demands in natural language format.
Data Source
AI summary
A method in an illustrative embodiment includes determining, using a language model, a plurality of quantitative representations of a plurality of demands described in a natural language, and classifying the plurality of demands by clustering into a plurality of tasks based on the plurality of quantitative representations. The method further includes determining, using the language model, a quantitative representation of each task among the plurality of tasks and a quantitative representation of each agent among a plurality of agents, and assigning the plurality of tasks respectively to corresponding agents among the plurality of agents based on the quantitative representation of each task and the quantitative representation of each agent. In embodiments of the present disclosure, demand analysis and task assignment are performed on natural language demands by using a language model, which can achieve efficient matching between tasks and agents.