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).


Victor Dibia

Principal Research Software Engineer @Microsoft Research

Victor Dibia is a Principal Research Software Engineer at Microsoft Research where his current work is focused on the design of multi-agent systems powered by Generative AI models. Victor is a core contributor to AutoGen - a leading python open source library for building multi-agent applications and the creator of AutoGen Studio, a low code interface for authoring, testing and debugging multi-agent workflows. 

Read more

From the same track


LLM Powered Search Recommendations and Growth Strategy

In this deep exploration of employing Large Language Models (LLMs) for enhancing search recommendation systems, we will conduct a technical deep dive into the integral aspects of developing, fine-tuning, and deploying these advanced models.

Speaker image - Faye Zhang

Faye Zhang

Senior Software Engineer


Navigating LLM Deployment: Tips, Tricks, and Techniques

Self-hosted Language Models are going to power the next generation of applications in critical industries like financial services, healthcare, and defense.

Speaker image - Meryem Arik

Meryem Arik

Co-Founder @TitanML


GenAI for Productivity

At Wealthsimple, we leverage Generative AI internally to improve operational efficiency and streamline monotonous tasks. Our GenAI stack is a blend of tools we developed in house and third party solutions.

Speaker image - Mandy Gu

Mandy Gu

Senior Software Development Manager @Wealthsimple


A Framework for Building Micro Metrics for LLM System Evaluation

LLM accuracy is a challenging topic to address and is much more multi dimensional than a simple accuracy score. In this talk we’ll dive deeper into how to measure LLM related metrics, going through examples, case studies and techniques beyond just a single accuracy and score.

Speaker image - Denys Linkov

Denys Linkov

Head of ML @Voiceflow, Linkedin Learning Instructor