experiment

To measure causal effects on agent behavior, you can run controlled experiments with repeatable baselines and variants. This skill isolates variables across multiple runs to build robust evidence of how specific constraints affect outcomes.

Designing controlled experiments for AI agents requires articulating a hypothesis, establishing a bare baseline, and creating constrained variants. You then execute trials and compile run data to identify causal relationships in agent behavior.

Reproducibility when testing model configurations is ensured by maintaining repeatable baselines and running multiple trials per variant. This structured approach generates traceable artifacts and robust evidence for your findings.

Yes, you can test multiple hypotheses and constraints across different agent runs. The skill applies to experiments that vary hypotheses, constraints, and model configurations to identify and measure causal relationships in behavior.

To structure uncertainty after an agent experiment, the skill requires a structured uncertainty section in the deliverables. This analyzes per-indicator detection rates and assembles a narrative to guide subsequent work and next steps.