A Compound AI System is an architectural approach that integrates multiple AI components—such as Large Language Models (LLMs), retrievers, databases, and external tools—to solve complex problems. Unlike a monolithic model that relies solely on its training data, a compound system breaks tasks into modular steps. This allows the system to access up-to-date information (via RAG), execute code, or trigger actions. By combining probabilistic models with deterministic logic, organizations achieve higher accuracy, trust, and flexibility in their enterprise applications.

What is the difference between a Compound AI System and a standard LLM?

A standard LLM is a single statistical model that predicts the next token based on training data. A Compound AI System wraps that model in a broader architecture. It gives the model access to "tools"—like search engines, vector databases, or calculators—allowing it to reason, fact-check, and perform specific actions rather than just generating text.

What are the main components of a Compound AI System?

These systems generally rely on the orchestration of three or more of the following distinct components:

• The Model: An LLM (like GPT-4 or Llama 3) acting as the reasoning engine.
• Retrieval Systems: Vector databases or search APIs to fetch relevant, private context.
• Tools: APIs that allow the system to interact with the outside world (e.g., send emails, query SQL).
• Orchestration Layer: The logic or framework (like LangChain) that manages the flow between these parts.

Why are enterprises moving away from monolithic models?

Monolithic models suffer from hallucinations, lack knowledge of private enterprise data, and cannot easily update their knowledge base. Compound systems solve this by grounding the AI in real-time data and providing audit trails for how answers were generated.

What are common examples of these systems in production?

Most modern "AI apps" are actually compound systems. Common examples include:

1. RAG Pipelines: Chatbots that look up internal documentation before answering.
2. Coding Agents: Systems that write code, run it to test for errors, and iterate on the solution.
3. Data Analysis Bots: Systems that convert natural language into SQL queries, execute them against a database, and graph the results.

How does Shakudo support the development of Compound AI Systems?

Building compound systems requires integrating many fragmented tools—vector stores, model endpoints, and data pipelines—which usually creates security risks and DevOps headaches.

Shakudo solves this by acting as a tool-agnostic operating system. We allow you to spin up and connect any component (open or closed source) within your own secure infrastructure. We handle the orchestration, governance, and networking, ensuring your compound system is production-ready, secure, and compliant in weeks, not months.