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Explore how Model Context Protocol (MCP) can revolutionize AI agent evaluation and self-improvement in this 14-minute conference talk. Learn about a new paradigm where complex AI evaluation frameworks become accessible to AI agents through MCP, enabling controlled self-improvement mechanisms that surpass traditional unstable self-criticism loops. Discover how MCP-accessible evaluation frameworks provide the crucial persistence layer needed to stabilize and standardize progress measurement toward plan fulfillment in agent systems. Examine practical demonstrations of MCP-enabled evaluation engines that allow agents to self-improve independently of their underlying architectures and frameworks. Understand the potential for this approach to become a fundamental component of rigorous agent development, offering a framework-agnostic solution for agent network stabilization and continuous improvement.
