A tax deduction that reads perfectly and matches what H&R Block's site says. Our engine still flagged it BLOCK, and it was right. 🚦 The position looked clean: the new OBBBA car-loan interest deduction is above-the-line and reduces AGI. It isn't. QPVLI is a below-the-line deduction under §63(b)(7), and it does not touch AGI. That is a documented consensus error mainstream tax-prep guidance has mislabeled. StatuteGuard is the verification layer that catches it. Not another tax-prep tool. Paste a position from any platform (ONESOURCE, CCH Axcess, Blue J, ChatGPT, or an internal model) and a deterministic policy engine returns PASS, BLOCK, or NEEDS-REVIEW against the encoded statute. Agent advises, code decides. The only AI step reads messy language into a structured claim. The verdict is readable policy code you can confirm against the statute, not an LLM grading its own homework. When it blocks, you see why. The citation chain animates §163(h)(1) → §163(h)(4)(A) → §63(b)(7) → §62/§63, and a 5-way downstream cascade lights up red: AGI, state tax, IRMAA, the medical-expense floor, student-loan IDR. Then it writes a filable §6662 audit record. This is the part that matters at signing time. The 20% IRC §6662 accuracy-related penalty lands on the human who signed the return, not the algorithm that drafted it. On our 42-case labeled golden set the layer resolved 71.4% of positions deterministically with zero false blocks, and escalated every genuine gray area to a human instead of guessing. If your team is weighing how to verify AI-drafted tax positions, not just draft them faster, we would like to hear how you are approaching it. #TaxTech #TaxCompliance #AIGovernance #AICompliance #TaxAI