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@article{180095,
        author = {Sahana Sharma and Preetham US and Pranav Jhadtheela and Manish Anand and Vishak Bharadwaj HN},
        title = {VSPEC: Versatile & Scalable Platform for Efficient AI Agent Creation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {83-90},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180095},
        abstract = {The proliferation of AI agents across 
industries has highlighted critical limitations in 
current development frameworks, including high 
technical 
barriers, 
integration complexity, and 
scalability bottlenecks. This paper presents V-SPEC 
(Versatile Platform for Scalable and Efficient AI 
Agent Creation), a novel framework designed to 
democratize AI agent development through visual 
program- ming interfaces and automated optimization 
systems. Through systematic analysis of 42 recent 
studies spanning 2022-2025, we identify key 
architectural challenges and propose innovative 
solutions via V-SPEC’s Model Context Protocol 
(MCP) server, hierarchical meta-agent system, and 
auto-prompt optimization engine. Our framework 
addresses the accessibility gap observed in platforms 
like LangChain and AutoGen by providing a no-code 
visual interface while maintaining enterprise-grade 
scalability. Preliminary benchmarks demonstrate 40% 
faster deployment cycles and 60% reduction in 
computational overhead compared to existing 
frameworks. The platform’s hierarchical agent 
architecture enables coordination of specialized sub- 
agents 
through 
standardized 
JSON-based 
communication protocols, facilitating seamless 
integration of diverse AI models and external services.},
        keywords = {AI Agents · Multi-Agent Systems · Visual  Programming · No-Code Development · Model  Context Protocol · LLM Integration},
        month = {May},
        }