Design Patterns for Compound AI Systems (Conversational AI, CoPilots & RAG)

How to build configurable flows and compound AI systems using open source tools.

Some common deployment patterns of these systems:

RAG (retrieval and understanding is key) - with access to Thought generation, Reasoning and contextual data, these systems self-reflect and try to understand a user’s query in advanced ways, before responding back with an answer, ideal set up is an Agent Assist system. When combined with user facing models / systems like a Dialogue model, a RAG system could be a part of a Conversational AI or a CoPilot system.

Multi-Agent Problem Solvers (collaborative role playing is key) - these systems rely on collaborative and automated build up of solutions based on outputs of agents being fed into each other with a well defined role and purpose. Each agent has access to it’s own set of tools and can assume a very specific role while reasoning and planning it’s actions.

Conversational AI (dialogue is key) automation softwares like customer service agents, which interact with humans within an app / ecosystem and execute repeatable tasks based on inputs and validations from humans. The most important aspect here is conversational memory and dialogue generation with the feeling of having a conversation with a human. The Dialogue Model can have access to an underlying RAG system, or a Multi-Agent problem solver.

CoPilots (a human in the loop interface is key) - with access to tools, data, reasoning and planning capabilities, and specialized profiles, these systems can independently interact with a human while solving problems with closed solutions. The key differentiator for becoming a CoPilot is understanding of the environment in which the human works.