There’s a Major Problem with Multi-Agent AI (Nobody’s Talking About It)

Multi-Agent AI sounds promising... but is it actually working?
In this video, I break down 3 major research papers that expose critical flaws in how large language models (LLMs) interact when placed in multi-agent systems. These failures aren’t just bugs, they reveal deeper issues in coordination, safety, and bias.

We’ll cover:

Why multi-agent systems fail up to 66% of the time
The MAST failure taxonomy (Specification, Alignment, Verification)
Group conformity and bias amplification in AI agents
Safety concerns revealed by Agent-SafetyBench
What this means for the future of AI tools like AutoGen, CrewAI, LangGraph, and beyond

Whether you're building agentic workflows, experimenting with LLM orchestration, or just curious about the future of autonomous AI, this breakdown is for you.

Research papers mentioned:

Why Do Multi-Agent LLM Systems Fail? : https://arxiv.org/pdf/2503.13657
An Empirical Study of Group Conformity in Multi-Agent Systems: https://arxiv.org/pdf/2506.01332
AGENT-SAFETYBENCH: https://arxiv.org/pdf/2412.14470

Let’s talk:
Have you built anything with multi-agent frameworks? What problems did you face? Drop a comment, I’d love to hear your take.
Business inquiries: katia@secondlifesoftware.com

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