Why most AI agents fail in production

Short summary

Most AI agent prototypes fail in production not because models are weak, but because they lack audit trails, approval gates, and governance. Production deployments require centralized workflow definitions, explicit reliability standards, and access controls—treating the agent as an operations system, not a prototype. Success comes from investing in governance and reliability before scaling, not as an afterthought.

• •Demo-first architecture fails: production needs audit logs, approval gates, and access controls
• •Vibe-coded workflows in multiple chat sessions become unmaintainable; standardize to one canonical version
• •Reliability beats speed for regulated work; invest in governance from v1, not as a v2 retrofit

Generated with AI, which can make mistakes.