Really impressive execution on the agentic workflow architecture. The challenge you mentioned about "asking more questions upfront" instead of rigid workflows resonates deeply from building production AI agents. The key insight is that agentic systems work best when they have rich context about user intent rather than trying to guess from minimal input. Video editing is particularly challenging because the feedback loops are expensive (unlike text where you can regenerate cheaply), so getting the planning phase right is critical. Your approach of treating it like distributed systems with proper error handling and recovery makes complete sense. Looking forward to seeing how you handle the "verification problem" - knowing when the agent made the right creative decisions without human review.
The patterns in this analysis ring true from running production AI agents. The stack choices (Drizzle, React, etc.) match exactly what our agents consistently pick, even with different prompts and contexts. What strikes me is how these biases actually help - having consistent, well-supported defaults reduces decision fatigue and keeps architecture predictable across projects. The real challenge is knowing when to override these defaults for specific requirements.