Context: In logistics, safety incident rate is commonly reviewed as a compliance number, but it reflects whether teams step in early enough to stop risk from becoming harm.

Challenges: Signals are dispersed across near-miss notes, shift handovers, and supervisor observations, accountability is unclear, and escalation waits for confirmation instead of responding to emergence, so the same conditions replay across multiple shifts before patterns become visible.

Solution: The Safety Execution Guidance Agent, an AI agent built using Microsoft Copilot Studio, monitors fatigue, near-miss, and equipment signals as they converge and intervenes during supervisor pause and reassignment decisions, flagging task drift before an incident materializes.

Impact: Logistics operators following this approach see teams act sooner and risk interrupted instead of recorded, with sustained safety performance coming from disciplined decisions in the moment, not post-incident correction.