The buzz around compound AI systems is real, and for good reason. Compound AI systems combine the best parts of multiple AI models, tools, and systems to solve complex problems that a single AI, no matter how powerful, might struggle to tackle efficiently.

Before diving into the magic of compound AI systems, let's rewind a bit and explore how application development has evolved. Remember the days of monolithic applications? These were giant, all-in-one software systems that handled everything -- front-end interactions, back-end processing, and database management -- within a single codebase. They were powerful, but they had their drawbacks.

This paved the way for Microservices Architecture, a game-changer that allowed businesses to split large, monolithic applications into smaller, self-contained services. Each microservice focused on a specific business function like user authentication or inventory management, offering flexibility and scalability that monolithic systems couldn't match.

But, microservices weren't without their challenges:

We're seeing the same evolution in the AI world, where large language models (LLMs) like GPT-4 and Meta Llama have become powerful generalists. They excel at handling a wide range of tasks, but, much like monolithic apps, they aren't perfect for every job.

Compound AI Systems are the GenAI version of microservices. These systems decompose AI tasks into specialized segments. Instead of relying on one giant model to do it all, several models, tools, and components are deployed, each optimized for specific tasks.

So, what does a compound AI system look like in practice? Picture a team of AI models, each excelling in a particular area, working together to solve complex tasks:

This modular approach allows compounded AI systems to break down complex challenges into smaller, manageable steps, much like how microservices revolutionized traditional application development.

One platform leading the charge is Databricks Mosaic AI. It gives businesses the tools they need to build production-quality compound AI systems by integrating multiple AI models, data retrieval systems, and external APIs.

To make this more concrete, let's take a look at a Maintenance Bot powered by Databricks Mosaic AI. The bot is built to assist with troubleshooting machinery, accessing repair manuals, and providing contextual insights.

This Maintenance Bot is a perfect example of a compound AI system that combines multiple AI components, such as vector embeddings, graph databases, and LLMs, to resolve complex user queries efficiently and intelligently.

Just like microservices transformed how we build applications, compound AI systems are transforming how we solve complex problems with AI. With specialized models and tools working together, we can build AI systems that are more flexible, efficient, and powerful.

And with platforms like Databricks Mosaic AI, companies can deploy these systems at scale, ensuring their AI solutions are not only cutting-edge but also production-ready. So, why settle for one brain when you can have a team of genius AIs working together? The future of AI is compound, and it's happening now.