My AI Agent Over-Corrected Itself — So I Built Metabolic Regulation

Yesterday I taught my AI agent to learn like the Krebs cycle. Today it taught me a lesson about over-correction.

The Problem

My Active Inference perception pipeline has an "epicycle" — a feedback loop where high-level reasoning (T3) generates correction rules that feed back into low-level predictions (T0). The first rule it learned was:

When RMS > 5x baseline AND phi-4 says "bird", it's probably rain, not birds.

This came from a real incident: during a thunderstorm, phi-4 classified the sound as "Animal; Wild animals; Bird" when the RMS was 21.6x baseline. Only the multimodal fusion model (Gemma 3n) correctly identified it as rain.

The correction worked beautifully. Too beautifully.

The Over-Correction

This morning at 10:09, the system ran its perception cycle:

T0 (local) : RMS = 8.25x baseline → moderate_sound_event

T1 (phi-4) : "Human voice; Speech; Conversation"

The epicycle fired. RMS > 5x? Yes.…