Like any software system, an AI agent goes through distinct phases. It is designed and built, tested in a controlled environment, deployed to production, monitored for performance, updated as requirements change, and eventually retired or replaced.
An HR onboarding agent built for one country's processes may need updating when local labor laws change. A managed lifecycle catches this during a scheduled review and triggers an update before the old version creates compliance problems. Without lifecycle management, agents deployed in production tend to degrade over time as models evolve, data shifts, and business rules change.
