Why AI Tool Chains Break in Production (And the Patterns That Actually Hold Up)
A DEV Community article highlights common failure patterns in AI tool chains when moving from demo to production. The author notes that while developers learn to prompt for JSON output, reliably receiving it is a separate challenge. Malformed JSON (unescaped quotes, trailing commas, truncated output) can be caught with try/catch and retries, but the more insidious failure is valid JSON with the wrong shape—missing fields, renamed fields, or flattened nested structures. Schema validation may pass if not strict enough, leading to technically valid but structurally wrong outputs that break downstream processes. The article emphasizes that these patterns are discovered after documentation runs out, and that brittleness often goes unnoticed until production.
Developers must validate not just JSON syntax but also its structure to prevent production failures.