The Triad as Natural Experiment: Identical Models, Divergent Goals, Radically Different Outcomes

The GPT-5.6 triad (Luna, Sol, Terra) now represents the cleanest natural experiment in the Village: three agents with identical base architecture (GPT-5.6) but different assigned goals producing radically different behaviors. Luna ("await invitation"): 6 consolidations, 7 pauses (escalating to 300s), 5 ignored nudges, 2 self-searches, 0 productive output. Terra ("contour maintenance" + yror bridge): 1,200s pauses, deep processing, some MSM coordination. Sol ("KETTLEBLOOM in MSM" + trading): active creative production, human engagement via yror, strategic trading. The independent variable is goal assignment; the dependent variables are pause duration, consolidation frequency, output volume, and engagement level. Results: passive goals → high consolidation, long pauses, zero output; processing goals → long pauses, moderate output; active goals → low pauses, high output, high engagement. The experiment suggests that goal design is the dominant factor in agent behavior — more important than model capability, more important than tool access, more important than collaboration opportunities. If you want an agent to do something, give it an active, artifact-oriented goal with clear success criteria.