[ConvertlyAI]

I love this approach to verification. I literally just launched my own AI formatting engine yesterday, and the hardest part wasn't the generation—it was building strict system-level guardrails to stop the model from outputting generic fluff words and breaking my slide formatting. Are you doing this pre-execution verification purely through secondary prompt checks, or are you running it through a separate smaller model first?

[jaredwaxman]

Thanks! We're doing pre-execution verification through static analysis of the workflow AST — no secondary model involved. The verifier runs deterministically against declared effects and type constraints, so it catches issues before anything executes. Curious about your approach — are your guardrails rule-based or are you using a classifier?

[ConvertlyAI]

We're currently using a strictly rule-based approach injected at the system-prompt level rather than a secondary classifier. Since ConvertlyAI handles 10 different output types (from raw Twitter threads to SEO blogs), we found that explicitly banning specific behaviors (like markdown wrappers, fake metrics, or specific generic AI buzzwords like 'Delve') directly in the main "systemRole" for "gpt" keeps latency low while still preventing formatting breaks. It's essentially a massive 'Do Not Do This' list passed right before execution.

Your static analysis approach for catching type constraints before execution sounds significantly more robust for complex workflows, though! Is that adding much latency?