Multi-Agent AI System for End-to-End Software Development Automation
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Solution Overview
Problem
Conventional software development lifecycle management techniques often focus on specific mechanisms, leading to incomplete automation and difficulty in integrating modern artificial intelligence, such as large language models, resulting in inefficient software development processes.
Innovation Solution
A multi-agent system leveraging large language models to automate software development lifecycles by receiving requests in natural language, determining tasks, generating software code, conducting unit tests, and releasing code based on predetermined guidelines, utilizing multiple decision-making agents in a shared computing environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional management techniques are used for software development lifecycle, then specific mechanisms can be managed, but end-to-end automation cannot be achieved and AI integration is difficult
Solution Approach 1:
The system segments the software development lifecycle into distinct phases (requirements, design, implementation, testing, deployment) and assigns specialized AI agents to each phase. Each agent handles specific tasks within its domain, enabling end-to-end automation through coordinated execution of segmented functions rather than attempting monolithic automation.
Solution Approach 2:
The patent creates a universal multi-agent framework where AI agents can perform multiple functions across different SDLC phases. The same agent architecture and interaction protocols are reused throughout the system, allowing agents to adapt to different tasks while maintaining consistent automation patterns, thus achieving end-to-end capability without proportionally increasing system complexity.
2Productivity
If conventional tools with narrow focus are used, then specific tasks can be handled, but overall software development lifecycle cannot be accelerated
Solution Approach 1:
The patent merges multiple specialized AI agents into a coordinated multi-agent system that covers the entire software development lifecycle. By combining agents for requirements analysis, design, coding, testing, and deployment into a unified framework with standardized communication protocols, the system achieves both high productivity in specific tasks and comprehensive lifecycle coverage simultaneously.
Solution Approach 2:
The system adds the dimension of AI intelligence to traditional SDLC tools, transforming conventional single-function tools into multi-capable AI agents. This dimensional enhancement allows agents to perform their primary function with high efficiency while also adapting to related tasks through natural language processing and learning capabilities, thus increasing both productivity and versatility.
3Extent of automation
If multiple AI models are integrated in a multi-agent system, then comprehensive automation is achieved, but system complexity increases
Solution Approach 1:
The patent introduces intermediary components including a task management layer, communication protocols, and coordination mechanisms that mediate between multiple AI models. These intermediaries standardize interactions between agents, manage task distribution, and handle coordination logic, thereby enabling comprehensive automation through multiple AI models while containing system complexity through structured mediation layers.
Data Source
AI summary
A method for automating a software development lifecycle by using artificial intelligence is disclosed. The method includes receiving a request to generate software components, the request including requirements in a natural language format; determining, by using a first model, tasks to generate the requested software components based on the request, each of the tasks including a sequence of subtasks, an acceptance criterion, and a predicted effort level; generating, by using a second model, sets of software code based on the tasks, the sets of software code corresponding to the requested software components; generating, by using a third model, unit tests for each of the sets of software code; initiating the unit tests for each of the sets of software code; and releasing, by using a fourth model, the sets of software code based on a result of the corresponding unit tests.