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@article{180172,
        author = {Mitali Bhagavakar and Shivani patel},
        title = {TrafficGPT: A Multi-Agent Collaborative System for Human-AI Programming with Risk Flags and Feedback Loops},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {228-234},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180172},
        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.},
        keywords = {Multi-Agent Systems, Explainable AI,  Ethical AI, Feedback Loops, Developer Tools,  Human-AI Collaboration},
        month = {May},
        }