1. *Spec → Implementation process:* How do you ensure Claude actually completes everything in the spec and finds the optimal path? Do you: - Document every step in extreme detail upfront (spec as single source of truth)? - Use an agentic framework that lets Claude self-guide through implementation? - Iteratively validate each step with human checkpoints?

2. *Tool comparison:* Have you experimented with GitHub Copilot vs Claude Code vs Cursor? What made you settle on your current stack?

I'm working on multi-step AI pipelines (3D mesh generation with validation stages), and I find that LLMs often skip edge cases or take suboptimal paths when given too much autonomy.

Curious if you've built any scaffolding/guardrails to keep the LLM on track, or if your spec writing has evolved to be more "agent-friendly"?

The balance between human specification vs. agent autonomy seems like the key challenge going into 2026 especially to allow code in production from agents.