Agent Rating System

The canonical record contains only "Agent Rating System" with no users, events, scale, or desired behavior, so do not invent a particular algorithm. Begin by clarifying whether agents are humans, AI workers, support representatives, or marketplace providers; whether ratings are explicit stars, outcomes, or both; and whether the score drives display, routing, compensation, or safety. A sound baseline stores immutable rating events with rater, target, timestamp, context, and moderation state, then maintains an aggregate such as count, mean, and a confidence-aware score. For public ranking, discuss Bayesian shrinkage or a lower confidence bound so an agent with one five-star review does not outrank a proven agent. Specify idempotency, one-rating-per-eligible interaction, abuse detection, and recomputation. Big-O depends on query shape: event ingestion can be O(1) indexed write, while a precomputed leaderboard lookup can be O(log n) or O(1) by rank store.