Large language models (LLMs) are impressive, but they aren't designed to handle the complexities of real-world enterprise applications on their own. Think of them as brilliant specialists—amazing at certain tasks, but needing a broader team to truly shine. That's where the concept of *compound AI systems* comes in. This new approach integrates LLMs into a larger network of components, including specialized models, data retrievers, databases, and other tools. Imagine an orchestra, with the LLM as a virtuoso soloist, supported by a talented ensemble. Researchers at Megagon Labs have introduced a blueprint for building these compound AI systems, offering a practical guide for enterprises. Their architecture focuses on seamless integration with existing infrastructure, efficient task orchestration, and cost-effective resource allocation. A key innovation is the use of 'streams' to manage the flow of data and instructions between different components. These streams act like communication channels, ensuring that all parts of the system work together harmoniously. Additionally, 'task planners' and 'data planners' optimize workflows, ensuring that tasks are assigned to the most appropriate agents and data sources, all while respecting real-world constraints like latency and cost. This blueprint is a significant step towards making AI more practical and powerful for businesses. It allows companies to leverage the strengths of LLMs while addressing their limitations, paving the way for more robust and efficient AI-driven solutions.