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Explore groundbreaking research from Duke University and Zoom Video Communications that reveals critical failures in MCP-enabled AI agents through comprehensive stress testing. Discover which large language models consistently underperform in multi-turn Model Control Protocol (MCP) agent scenarios and learn about the systematic diagnostic approach used to identify these weaknesses. Examine the LIVEMCP-101 research methodology that puts AI agents through challenging queries to expose their limitations and understand why certain LLMs struggle with complex, multi-step interactions. Gain insights into the current state of AI agent reliability and the specific technical challenges that cause these systems to fail when handling demanding real-world scenarios.
