Traditional Machine Learning (ML) and Deep Learning (DL) models offer high accuracy in specialised domains but their operational isolation constrains their utility in dynamic real-world workflows. This paper present a decoupled framework called “Modular-Agent” to automatically convert standard AI endpoints into fully functional, LLM-agnostic autonomous systems. The framework consumes standard model endpoints and extends them with dynamic agentic capabilities such as contextual memory, semantic routing and tool-use through open-source orchestration frameworks such as LangChain, LangGraph and Agno. The actual controller is a sophisticated LLM. This transforms a predictive static tool into an interactive agent. Empirical evaluations show that our automated ingestion pipeline reduces the manual integration boilerplate from over 400 Lines of Code (LOC) to just 14 LOC, facilitating faster deployment timelines. Moreover, the latency analysis indicates that the cognitive routing has little overhead and the total response time is less than one second (~ 447ms). The framework can efficiently package agents into containerised formats with a 95th percentile response latency of 850ms under peak workloads of 1,000 concurrent requests, showing its viability for scalable deployment across AWS, Azure, and GCP. The results conclude that modular agentic wrappers offer a highly scalable solution to modernise legacy AI infrastructures.

agentic AI,LLM orchestration,LangChain,LangGraph,autonomous systems,cloud deployment,modular architecture,tool-use,containerization