Experiment design for future bets

Problem

Potential future directions for YAML versioning and migration — such as automatic schema evolution, backward-compatible additive-only contracts, or live migration during compilation — represent significant investment but have uncertain payoff. There is no framework for designing lightweight validation experiments that could de-risk these bets before committing engineering resources. Without structured experimentation, decisions about versioning strategy will be based on intuition rather than evidence, increasing the chance of costly direction changes later.

Context

• The larger future bets for the YAML contract, compiler/normalizer, execution adapters, and release/versioning 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/core/, schema/compiled types, docs, and core test suites, 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 the YAML contract, compiler/normalizer, execution adapters, and release/versioning 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

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