Deployment gating for AI is the practice of requiring AI quality checks before shipping a model, prompt, tool, retrieval, or orchestration change. Traditional deployment gates check tests, type safety, and infrastructure health. AI deployment gates also check semantic behavior.
For agents, gates should include trajectory-level checks, not just final answer checks. A change might produce the right response while using the wrong tool, exposing data it should not access, or taking a path that is too slow or expensive.