Experiment design for future bets

Problem

The team has no lightweight way to test whether a proposed MCP capability or eval approach will actually work before committing to full implementation. Ideas like agent-driven anomaly detection, automatic dashboard optimization, or LLM-as-judge eval scoring sound promising but carry high uncertainty. Without designed experiments — controlled scope, success criteria, time-boxed effort, and measurable outcomes — the team either skips risky bets entirely (missing upside) or commits fully to ideas that fail late (wasting effort). A library of pre-designed experiment templates for the eval and MCP framework would let the team validate assumptions cheaply.

Context

Possible Solutions

Plan

Implementation Progress

Review Feedback

• Review cleared