The reliability gap: what it actually takes to put an AI agent in production

Short summary

Agents fail silently and non-deterministically in production, requiring measurement-driven approaches. Build eval sets from real traffic and wire them into CI to catch regressions early. Real safety comes from code guardrails—allowed-list tools per state, enforce schemas before execution, bound loops, and design human checkpoints where liability lives.

• •Agents require measurement and eval sets, not intuition or better prompts
• •Build eval sets from real production traffic and wire into CI for regression testing
• •Safety comes from code guardrails (tool allowlists, schema validation, bounded loops, human checkpoints), not prompts alone

Generated with AI, which can make mistakes.