This repository presents a conceptual guide, complemented by practical resources, for architecting robust multi-agent systems. The focus is not on building an individual agent, but on the unique challenges and effectiveness of orchestrating, governing, and scaling systems where multiple specialized agents interact to solve complex problems. You will find actionable guidance for designing for change, balancing long-term extensibility with pragmatic, shipping-first engineering.
The recommendations are grounded in production-scale, real-world solutions built in collaboration with Microsoft customers. As such, the approaches offered in this reference are both opinionated (benefiting from field experience) and agnostic (applicable across enterprises, technology stacks, and frameworks).
This guide is intended for software architects, software engineers and data scientists familiar with agentic services design and development. It is aimed at those with experience in building and deploying agents, whether they aim to extend existing systems to multi-agent architectures or build them from the ground up.
Note:
Generative AI is advancing rapidly, with new models, patterns, protocols and paradigms constantly emerging. While the current design is intentionally agnostic and broad, we expect to refine and improve it as the ecosystem matures.
If you want to jump straight to the architecture reference, check out the Reference Architecture chapter. Otherwise, if you'd like to explore the concepts and recommendations in more detail, just keep reading the next chapters.
