Will Agent evaluation via MCP Stabilize Agent Networks? - Ari Heljakka Introduction to Agent Evaluation 00:04
Ari Heljakka, CEO of Fruit Signals, discusses the role of the Model Conduct Protocol (MCP) in stabilizing agent networks and swarms.
The need for stable agent swarms is highlighted, as they are crucial for solving complex knowledge work problems.
Challenges in Evaluating Agents 00:30
Current agent swarms often lack stability when addressing complex problems due to observation limitations and dynamic environments.
There's a difficulty in comprehensively testing agents and ensuring they consistently progress toward goals.
Evaluation Framework 02:13
Effective evaluations require a systematic approach rather than simply adding evaluation stacks.
A clear framework for setting up evaluators is essential, such as those for a hotel reservation agent, which includes policy adherence and output accuracy.
Stabilization Loop Concept 04:25
The stabilization loop involves agents completing tasks, receiving evaluations in the form of numeric scores and feedback, and improving their performance based on that feedback.
The MCP serves as the method for linking agents to the evaluation framework.
Practical Examples of Evaluation 05:19
An experiment demonstrates using text evaluations without code to measure and improve a marketing message.
The process involves using the MCP interface to access evaluators and improve the original message based on scores.
Live Agent Example 09:12
A hotel reservation agent is tested with and without the MCP to illustrate the difference in performance.
Without the MCP, the agent incorrectly recommends a nearby hotel; with the MCP, it adheres to its booking policy and avoids mentioning the competitor.
Summary of Key Steps 12:34
Ensure the evaluation platform is powerful enough to support diverse evaluators and their lifecycle management.
Start by running evaluations manually to understand their functioning before integrating them with agents through the MCP.
This approach aims to enhance control, transparency, and self-correction in agent behavior.