Large language models (LLMs) like ChatGPT have captured the world's imagination. But behind the conversational facade lies a complex system begging for a smoother integration into real-world applications. Imagine LLMs not just as standalone chatbots, but as intelligent gateways connecting you to a whole ecosystem of apps and services. This is the vision behind a new approach to middleware for LLMs, designed to make them more accessible, powerful, and adaptable within enterprise settings. Currently, deploying and managing LLMs is a considerable undertaking. From resource allocation and scaling to integrating with existing services, the process is fraught with challenges. Think of it like trying to fit a powerful, cutting-edge engine into a vintage car – the power is there, but harnessing it effectively requires a significant overhaul. This new middleware acts as the necessary bridge. It tackles issues like efficiently distributing LLM workloads across GPUs, managing conversational state, and caching responses to optimize performance. It also simplifies the integration of LLMs with existing enterprise software, translating natural language prompts into the language of APIs and network protocols. One particularly innovative aspect of this approach is the concept of the LLM as a gateway. In this scenario, the LLM doesn't just respond to queries; it acts as a central hub, routing requests to the appropriate services and orchestrating their execution. Imagine asking your LLM to generate a sales report, and it automatically pulls data from your CRM, formats it, and delivers the results – all through a simple natural language prompt. This approach moves beyond simply adding an LLM to an existing system; it reimagines the LLM as the core interface for interacting with a diverse range of applications. A proof-of-concept implementation using a calculator service demonstrates the potential of this approach. By connecting an LLM to a calculator, the system can accurately handle complex mathematical queries that would stump the LLM alone. This illustrates how integrating external services can significantly enhance LLM capabilities and open doors to a broader range of applications. While this research is still in its early stages, it offers a compelling glimpse into the future of LLM deployment. As LLMs become increasingly sophisticated, a robust and adaptable middleware will be essential to unlock their full potential and integrate them seamlessly into the fabric of our digital world.