This change enriches conditional block outputs by tracking and exposing detailed per-branch evaluation data—rendered Jinja expressions, LLM‐based reasoning, boolean results, prompts, and raw responses—so users can see exactly how each branch was assessed. It significantly improves debugging transparency and LLM‐evaluation accuracy (via chain‐of‐thought prompting and a slimmed context snapshot) while integrating seamlessly into the existing workflow SDK. Potential concerns include added complexity and performance overhead from extra rendering and LLM calls, risks around securely masking sensitive data, and ensuring new error paths are robustly tested.