Will Agent Evaluation via MCP Stabilize Agent Frameworks?

About This Presentation

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.

Watch Recording

Key Topics Covered

• Model Context Protocol (MCP) and its role in agent evaluation
• Stabilizing agent frameworks through controlled self-improvement
• MCP-accessible evaluation frameworks for persistent progress tracking
• Architecture-independent agent improvement methodologies
• The future of rigorous agent development

About the Speaker

Ari Heljakka presents insights from Root Signals' work on agent evaluation and the Model Context Protocol. This presentation was delivered at the AI Engineer World's Fair, showcasing cutting-edge research in agent evaluation methodologies.