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TrafficGPT: A Multi-Agent Collaborative System for Human-AI Programming with Risk Flags and Feedback Loops

  • Unique Paper ID: 180172
  • Volume: 12
  • Issue: 1
  • PageNo: 228-234
  • Abstract:
  • The integration of Large Language Models (LLMs) into Software Engineering (SE) workflows has unlocked unprecedented opportunities for automation, collaboration, and productivity. However, existing tools like GitHub Copilot and ChatGPT operate in isolation and lack trustworthiness, explainability, and adaptive learning. We propose a novel Adaptive, Ethical, and Explainable Multi-Agent System (MAS) tailored for intelligent software engineering collaboration. Our system utilizes a set of specialized LLM-powered agents—such as a Debugger Agent, Refactoring Agent, Optimizer, Documentation Agent, and Ethics Agent—to streamline complex SE tasks. The MAS architecture is supported by a Payload CMS backend, a custom Explainable AI (XAI) layer, a risk flagging mechanism, and a developer override interface to ensure human control and accountability. In addition, the system implements an adaptive learning loop that fine-tunes agent behavior over time based on user feedback. Our prototype demonstrates that this approach significantly improves trust, usability, and development efficiency. The paper presents architectural insights, implementation results, and a comparison with existing LLM-based SE tools.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 12
  • Issue: 1
  • PageNo: 228-234

TrafficGPT: A Multi-Agent Collaborative System for Human-AI Programming with Risk Flags and Feedback Loops

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Journal no 47859

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