A recent research paper from Shanghai Jiao Tong University and the ANP Community provides the first comprehensive analysis of existing agent protocols, offering a systematic two-dimensional classification that distinguishes between context-oriented versus inter-agent protocols and general-purpose versus domain-specific protocols.

The paper highlights a critical issue in the rapidly evolving landscape of LLM agents: the lack of standardized protocols for communication with external tools or data sources. This standardization gap makes it difficult for agents to work together effectively or scale across complex tasks, ultimately limiting their potential for tackling real-world problems.

Rubén Fernández (@rub) recently shared insights on a Microsoft paper about AI Agent failure modes, concerned it might not get the attention it deserves. You can find his original note here: https://substack.com/@thelearningrub/note/c-113284290

He mentioned:

I liked Microsoft’s paper about Failure Modes of AI Agents, but I think it will go unnoticed by most people, so I’ll prepare small infographics to showcase the information it contains.

The first one, some Effects of AI Agents’ failure