How I built an agent that works at Amazon while I sleep (10 steps)

A 10-step framework for building autonomous AI agents that handle repetitive engineering tasks like ticket triage, code implementation, and PR reviews. The process starts with understanding manual workflows, progresses through prompt refinement and tool integration (MCP servers, agent skills), and culminates in scheduled multi-agent systems. The approach emphasizes iterative development, creating specialized agents with clear handoffs, and positioning humans as supervisors rather than operators. Key concepts include the Ralph Wiggum loop for continuous execution, agent SOPs for orchestration, and defining clear input/output boundaries for each agent.