When you ask an AI assistant like Kiro (AWS's AI coding assistant), Claude Code, or ChatGPT to "build me an agent," you get working code. But you don't see the architecture decisions happening behind the scenes. The agent responds to queries, but it might waste tokens in reasoning loops, hallucinate answers from incomplete data, or freeze on slow APIs. These failures are silent until production. When you prompt AI coding assistants to build agents, they make architecture decisions silently—choosing retrieval strategies, validation approaches, and error handling patterns. These 8 patterns give you the vocabulary to specify production-grade decisions in your prompts, preventing hallucinations and token waste before code is generated. This post closes two series I wrote documenting the most expensive agent failures in production: Stop AI Agent Hallucinations (5 techniques) and Why AI Agents Fail (3 failure modes) . If you know these 8 patterns, you can guide AI assistants to avoid them from the start.…