Building Efficient AI Agent Systems: Lessons from UserJot

UserJot experimented with building a multi-agent AI system to analyze customer feedback at scale and auto-generate changelogs. The author shares key learnings, centering on a two-tier architecture: primary agents manage context and task decomposition, while stateless sub-agents focus on single tasks. Efficiency comes from task decomposition (vertical and horizontal), structured communication protocols, agent specialization, and orchestration patterns like MapReduce. The article stresses statelessness, context management strategies, and error handling, offering performance optimization tips and monitoring metrics.