What actually breaks when you put AI agents in production

Short summary

Production AI agents fail from hallucination chains, unguarded write access, and missing observability—not weak models. Key patterns: validate all LLM outputs against schemas, separate read (planning) from write (mutations), trace every agent step for replay, right-size models per task, and define success metrics before coding. Production reliability comes from standard engineering discipline—input validation, least-privilege access, observability, resource budgets—applied to an unpredictable dependency.

• •Validate all LLM outputs; treat them as untrusted input against schemas
• •Separate read access (planning) from write access (state mutations) with approval gates
• •Log and trace every agent step for observability and incident replay

Generated with AI, which can make mistakes.