I Turned Temperature Up to Save My Extractions: The 3‑Node LangGraph That Trades Variance for Truth

I noticed it in the most annoying way: the email clearly contained a job title — not perfectly, not cleanly, but enough — and my extraction came back with nothing. Not wrong. Not malformed. Just… absent. That’s the failure mode that hurts in enterprise systems, because downstream code can’t tell the difference between “not present” and “present but partially occluded.” Silence looks like confidence. This is Part 2 of my series, “How to Architect an Enterprise AI System (And Why the Engineer Still Matters)” . In Part 0 (“Continuity Is a Feature”) , I dealt with the brutal reality that enterprise workflows aren’t stateless: threads span days, people reply out of order, and the system has to remember what it already learned. In Part 1 (“Adversarial Emails and the Front Door Problem”) , I hardened the intake boundary so forwarded threads, disclaimers, and hostile payloads don’t poison the run. Here in Part 2, I’m going to show the decision that surprised me most: High variance + downstream