Multi-Agent Code Generation Platform for Hallucination Control
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Solution Overview
Problem
Current Large Language Models (LLMs) face challenges in generating high-quality code, particularly in architecture-dependent languages, suffer from hallucinations, and are resource-intensive, with concerns over data security and the need for multiple stepwise procedures to achieve near-perfect results.
Innovation Solution
A platform with multi-agent support utilizing a base operating system, AI framework, and agent applications that include input, processing, and feedback units, enabling parallel execution of agents to manage LLMs efficiently, minimize hallucinations, and enhance code security and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are used for code generation, then code generation capability is improved, but code quality varies by language and hallucinations occur
Solution Approach 1:
The patent segments the code generation process into multiple independent agents: requirement analysis agent, system design agent, code generation agent, and test generation agent. Each agent specializes in a specific aspect, ensuring consistent quality across different programming languages by applying domain-specific expertise rather than relying on a single general-purpose LLM.
Solution Approach 2:
The patent introduces a project manager agent as an intermediary that coordinates between different specialized agents and manages the overall code generation process. This mediator ensures that requirements are properly translated across different language contexts and that quality standards are maintained consistently regardless of the target programming language.
2Manufacturing precision
If model fine-tuning is performed to improve LLM performance, then code generation quality is improved, but time and resources are consumed
Solution Approach 1:
Instead of fine-tuning a single large model, the patent divides the task across multiple pre-trained specialized agents, each optimized for specific aspects (requirements, design, coding, testing). This eliminates the need for extensive fine-tuning while maintaining high code generation quality through specialized expertise in each domain.
Solution Approach 2:
The agents are pre-configured with specialized capabilities and knowledge bases before execution. The project manager agent is pre-programmed with coordination logic and quality standards, allowing the system to deliver high-quality code generation without requiring time-consuming fine-tuning during deployment.
3Adaptability or versatility
If commercial LLMs are used for code generation, then code generation capability is improved, but data security concerns arise
Solution Approach 1:
The patent extracts the code generation capability from external commercial LLMs and implements it through local specialized agents that run within the corporate environment. This extracts the security risk by removing dependence on external services while maintaining code generation capabilities through locally-deployed intelligent agents.
Solution Approach 2:
The project manager agent acts as an intermediary that manages all interactions between the specialized agents and external systems. This intermediary enforces security protocols, controls data flow, and ensures that sensitive corporate data remains within the secure boundary while still enabling code generation functionality.
4Manufacturing precision
If multiple stepwise procedures are used to achieve near-perfect code generation, then code quality is improved, but development complexity increases
Solution Approach 1:
The patent merges multiple stepwise procedures into a unified multi-agent system where requirement analysis, system design, code generation, and test generation are combined into coordinated agent workflows. This integration maintains the thoroughness of multiple steps while reducing perceived complexity through unified management by the project manager agent.
Solution Approach 2:
The project manager agent provides universal coordination across all code generation stages and programming languages. This multi-functional agent handles task allocation, quality control, and process management uniformly, simplifying the overall process complexity while maintaining high code generation quality across diverse requirements.
Data Source
AI summary
Disclosed herein is an apparatus and method for automatically generating code based on artificial intelligence (AI) agents. The apparatus includes two or more agents configured to receive and convert a natural language prompt upon an event trigger, input the converted prompt into a large language model, process the output, and return the result to the user. The apparatus further includes a graphical user interface (GUI) in which functions for code generation are defined in the form of tasks. The GUI transfers event triggers to corresponding agents based on tasks selected by the user and displays the returned data. The agents are mapped to tasks and may operate in conjunction with other agents by exchanging events.