Will Agent Evaluation via MCP Stabilize Agent Frameworks?

Exposing complex AI Evaluation frameworks to AI agents via MCP allows for a new paradigm of agents to self-improve in a controllable manner. Unlike the often unstable straight-forward self-criticism loops, the MCP-accessible evaluation frameworks can provide the persistence layer that stabilizes and standardizes the measure of progress towards plan fulfillment with agents. In this talk, we show how MCP-enabled evaluation engine already allows agents to self-improve in a way that is independent of agent architectures and frameworks, and holds promise to become a cornerstone of rigorous agent development.
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