Modernizing legacy software is often framed as code migration, an approach that assumes access to source code and an understanding of system internals. In practice these assumptions fail: source may be inaccessible, data in proprietary formats, and documentation dispersed. We study an underexplored regime in which a replacement is aligned to a legacy system treated as a black-box oracle, using only its input–output behavior.

We present CORAL (Closed-loop Oracle Alignment), an agentic framework that aligns a modern U.S. individual income tax engine (Form 1040) to a production oracle (a system built over decades) by iteratively refining LLM-bootstrapped form schemas. Each round runs a scenario on both systems, compares e-file output field-by-field, and diagnoses discrepancies against an authoritative IRS knowledge base (KB), grounding every fix in published regulation rather than relying on model knowledge. An edit is kept only if re-execution shows no meaningful field-level F₁ regression and all tests pass, a deterministic check rather than model self-assessment. Round-robin sequencing over under-covered forms then reaches the long tail of forms, not just the high-frequency ones a taxpayer shares with everyone.

Across 119 tax-expert-authored scenarios, each a complete taxpayer return, CORAL repairs defects from single-field corrections to reviving entire non-functional forms (53 fixed; 13 escalated to human review, ensuring ambiguous rules and input-data gaps are audited manually), lifting macro-F₁ on a 30-scenario held-out test split, averaged over returns, from an LLM-bootstrapped start (0.17) to 0.90.