This paper presents an agentic AI–driven framework for autonomous generation of sandbox-ready API simulations in enterprise environments. Traditional third-party API integration relies heavily on manual mock creation, static test environments, and repetitive validation processes, resulting in increased operational costs, delayed developer onboarding, and inconsistent testing experiences. The proposed system leverages a multi-agent architecture to ingest API specifications, perform automated validation and correction, and dynamically generate executable sandbox APIs that closely mimic real-world behavior. The framework integrates intelligent contract analysis, synthetic data generation, and real-time environment provisioning to create scalable and production-like simulation environments with minimal manual intervention. Experimental evaluation demonstrates significant improvements in integration efficiency, including reductions in onboarding time, operational overhead, and maintenance effort compared to conventional approaches. The system is designed to be adaptable across different database dialects and enterprise platforms, enabling rapid deployment and extensibility. This work contributes to the field of enterprise AI systems by introducing a scalable, cost-efficient approach to API virtualization and simulation, with practical implications for developer experience, system testing, and digital transformation initiatives.
Sundar Ray Swapneswar (Sat,) studied this question.