What we built. A long-horizon agent that follows one fruit photo by photo and decides, every day, how hard to look. Every frame gets a cheap local look; the expensive model runs only when the agent's memory of that fruit says a change is worth it. A low-cost day is not a "healthy" verdict: the open question stays in memory until it is answered.
How it works. Each day (1) Liquid LFM2.5-VL-1.6B runs locally on the photo and returns visible features, anomaly candidates and uncertainty, about 2 s per frame at zero API cost. (2) The controller restores the fruit's record from Tinybird RawTree MCP and applies explicit rules: new anomaly, change that persists or grows, open question due, too long since a precise look. (3) It either stops there or calls GPT-5 with the original photo plus the past frames the memory points to. (4) Result, reason, open question and next check condition are written back to RawTree, so the agent survives restarts.
Nimble found, verified and fetched the data: 7,516 apple photos (Manalagi, CC BY 4.0, task-id per file) and a Zenodo record of the same tomato photographed on 18 consecutive days (CC BY 4.0, MD5-checked).
Results. On the same 18-frame sequence, baseline calls GPT-5 every frame; our agent calls it 5 times. Apple: 18 to 5 calls, $0.0321 to $0.0125 (61% lower). Tomato: 18 to 5 calls, $0.0227 to $0.0081 (64% lower). No disease labels, so we report calls and cost, not detection accuracy.
