Developer shares three-stage validation layer to prevent AI agent output failures

A software developer writing for DEV Community has outlined a recurring flaw in AI agent codebases where model responses are trusted without validation, causing runtime errors on edge cases. The core issue is that large language models like Claude and GPT-4 can hallucinate data structure rather than just content, returning null or semantically incorrect values even when using structured output modes. The author argues that schema-enforced JSON alone is insufficient because it validates types but not semantics, and many LLM workflows still rely on free-text parsing. To address this, the developer proposes a parse-validate-classify pipeline implemented in TypeScript using the Zod library, which forces calling code to explicitly handle both success and failure outcomes. The approach is presented as a practical safeguard applicable to any multi-step or tool-calling AI agent architecture.