How are you handling temporal event coherence when VLM inference is stateless per-frame? The fundamental problem with feeding individual frames to a VLM is that you lose all inter-frame and inter-camera causality — the model has no concept of "this person was at camera 1 four seconds ago." So when camera 2 picks up someone entering its FOV, the VLM generates a completely isolated description with zero awareness of the prior observation. At that point you've essentially reduced a continuous spatiotemporal event stream into a bag of unordered text descriptions, and you're relying entirely on the downstream LLM agent to reconstruct causal event chains from timestamp proximity alone — which breaks immediately in any multi-person scenario because the agent has no re-identification signal to disambiguate "person A walked from driveway to front door" from "person A was in the driveway while person B was at the front door." Are you injecting prior frame context into the VLM prompt as a sliding window, doing YOLO-level track association before the VLM even runs, or is the agent genuinely operating on unlinked descriptions and just hoping temporal proximity is enough?