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

• The larger future bets for AI agent tool interfaces, execution workflows, and eval-driven behavior tuning should be validated with scoped experiments before they absorb major implementation effort or become roadmap commitments.
• This task should design the experiments, not run them: define hypotheses, success signals, cheap prototypes or evaluation methods, and the decision rule for what happens next.
• Expected touchpoints include dataface/ai/, MCP/tool contracts, cloud chat surfaces, eval runners, and prompt artifacts, opportunity/prerequisite notes, eval or QA harnesses where relevant, and any external dependencies required to run the experiments.

Possible Solutions

• A - Rely on team intuition to pick which future bet to pursue: fast, but weak when the bets are expensive or high-risk.
• B - Recommended: design lightweight validation experiments for the strongest bets: specify hypothesis, method, scope, evidence, and the threshold for continuing or dropping the idea.
• C - Build full prototypes for every future direction immediately: rich signal, but far too expensive for early-stage uncertainty.

Plan

1. Choose the future bets for AI agent tool interfaces, execution workflows, and eval-driven behavior tuning that are both strategically important and uncertain enough to justify explicit experiments.
2. Define the hypothesis, cheapest credible validation method, required inputs, and success/failure signals for each experiment.
3. Document the operational constraints, owners, and follow-up decisions so the experiment outputs can actually change roadmap choices.
4. Rank the experiments by cost versus decision value and sequence the first one or two instead of trying to validate everything at once.

Implementation Progress

Review Feedback

• [ ] Review cleared