PydanticAI vs LangChain - Choosing an Agent Framework for Production, Not Demos

In a recent audit, a team showed me an AI assistant they'd built on top of their company knowledge base. The demo had landed well: ask how to use a feature, and it walked through the exact pain point their support queue kept seeing. Leadership signed off. In production, the same agent told a user to open a menu option that didn't exist. Not a vague answer - a specific UI path, stated with confidence. Nobody caught it in testing. It surfaced when I audited the system, not when a user complained. The prototype passed testing because nobody was checking whether the answer matched the product. In production, that gap becomes a liability: the model invents UI paths, and your backend has no schema to reject them. When you're choosing an agent framework, popularity is the wrong scorecard. Pick the one that fails loudly in development and gracefully in production - or you'll find out in audit. What "Production-Ready" Actually Requires Tutorial agents are built to impress in a fifteen-minute demo. Production agents run unattended, handle bad inputs, and ship answers your backend has to trust. The gap between those two goals is where most teams stumble - and it's rarely visible until something reaches a user. When I audit agent codebases, I evaluate five things the tutorials skip: Structured, validated outputs: Can your system reject an invented menu path before it becomes user-facing advice? Dependency injection for testing: Can you swap the knowledge base for a mock in CI without rewiring the agent? Retry and error handling: When the model returns malformed output, does the framework retry - or do you ship a parser exception? Observability hooks: Can you trace which document grounded a bad answer when support escalates? Type-checker support: Will static analysis catch a breaking API change before deploy, or after the agent silently misbehaves? If you want to score your own system, the Production Readiness Audit covers the same five categories - deployment, observability, fa