10 Reasons Your Multi-Agent Workflows Fail and What You Can Do About It

Multi-agent systems – a setup where multiple agents (generative AI models with access to tools) collaborate to solve complex tasks – are an emerging paradigm for building applications. Tools and frameworks like AutoGen make the development of multi-agent workflows more readily accessible to developers.

However, transitioning from experimentation to the development of reliable, production-ready systems remains challenging and somewhat unclear. As teams embrace and experiment with multi-agent systems, an increasingly important first step is to understand when and why this paradigm might fail. This talk highlights 10 common reasons these systems often fail based on early user feedback and the author’s work as a core maintainer of the AutoGen open-source Python framework (>1 million downloads, > 300 active contributors, > 18k users on Discord).