The first time a robot attempted "Wine Bottle in Bowl," its success rate was 15%. Then it crossed a threshold — and jumped to 95%. Not because the model was retrained. Because the code-based harness governing its execution had evolved itself. Today's VLA (Vision-Language-Action) models run open-loop. When a robot fails, there's no real-time correction — the feedback loop closes only after the episode ends, when a human reviews logs and manually adjusts parameters. That cycle is slow. It doesn't scale. Minor physical disturbances cascade into failures the system can't catch in the moment. Zetta ζ solves this across three timescales. During execution, high-frequency runtime critics monitor trajectory deviations and trigger recovery interventions before failures compound (action level). Failed rollouts are clustered by failure signatures, diagnosed through six causal layers, and the harness is minimally repaired (episode level). Only skills that generalize across held-out environments pass through the validation gate and are permanently committed to skill memory (iteration level). Base model weights stay frozen throughout. Only the code-based harness evolves. LIBERO-Pro: 34.5% → 90.8%. RoboCasa: 73.6% → 93.6%. Inference speed: 11.1× faster than the RPent baseline. Throughput: 1.7 → 35.1 episodes/min (20.6×). Learned skills transferred zero-shot across PnP-Sink, PnP-Cabinet, and PnP-Toaster tasks. Zetta ζ: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence #EmbodiedAI# #Robotics#