阻止 AI 代理在生产环境中失控的 7 种模式

AI agents may perform flawlessly during development, passing all tests and impressing stakeholders in demo scenarios. However, once deployed, they can quickly lead to significant issues, such as incurring $400 in API costs due to recursive loops, mistakenly emailing customers their neighbors' personal data, or generating SQL queries that drop indexes on production databases. This scenario is not hypothetical and reflects a common pattern in the industry as of 2026. The gap between 'demo-ready' and 'production-ready' AI agents is more pronounced than many teams realize, with fundamentally different failure modes compared to traditional software. Unlike REST APIs, AI agents can misinterpret questions, and database drivers may hallucinate table names, leading to unexpected outcomes.