From 7 Days to 30 Minutes: Autonomous Tax Logic Modernization at Enterprise Scale

For a leading tax solutions provider, modernizing a decades‑old COBOL‑based corporate tax calculation engine had become an urgent operational necessity. The legacy system—built and extended over several decades—processed intricate tax rules across more than 50 U.S. state and federal jurisdictions.

But the modernization effort faced several systemic obstacles:

• COBOL‑to‑MSTX conversion required 7–10 business days per jurisdiction, creating a years‑long modernization timeline.
• Tax logic was spread across 8+ legacy file formats, making interpretation and translation highly specialist‑dependent.
• Structural differences between COBOL and MSTX demanded precise, rule‑by‑rule translation, where even a small operator or label mismatch could break downstream calculations.
• Manual processes introduced recurring errors—duplicate declarations, incorrect row numbers, missing operators—each representing a potential compliance risk.
• Outputs had to pass two independent validation engines, but manual QA could not scale to meet increasing conversion volume.

The complexity, scale, and compliance sensitivity of tax computations made traditional modernization unsuitable. A new, intelligent, automation‑driven approach was required.

Evoke Technologies engineered and deployed a 9‑agent AI‑powered modernization pipeline, built on Anthropic Claude and designed specifically to interpret, translate, validate, and optimize complex tax‑rule logic at scale.

Multi‑Agent COBOL Understanding & Translation

Each AI agent was assigned a specialized role within the pipeline:

• COBOL structure analysis
• Legacy format parsing (across 8 file types)
• MSTX code generation
• Dependency graph reconstruction
• Rule-level semantic consistency checks

This ensured deep, context‑aware translation—not just syntactic conversion.

Automated Dual‑Engine Validation

A dedicated validation layer compared outputs across two independent engines:

• Engine A: MSTX ruleset validation
• Engine B: Syntactic & semantic verification

Detected issues triggered an AI‑driven auto‑fix loop, which:

• Diagnosed root causes
• Mapped them to 45 proven fix patterns
• Applied corrections across up to 3 iterative self‑healing cycles

This eliminated the need for manual debugging in most cases.

Continuous Learning Through Skill Libraries

The system was designed to get smarter with every conversion:

• 17 skill libraries stored reusable logic, translation patterns, and structural rules.
• 62 reference files served as ground‑truth examples across state and federal jurisdictions.
• New scenarios automatically updated the knowledge base, ensuring mistakes were never repeated.

This transformed tribal, specialist‑only knowledge into permanent, organization‑wide assets.

The organization now has a repeatable, scalable, AI‑first modernization pipeline capable of supporting large‑scale tax compliance programs without adding headcount or relying on scarce mainframe specialists.

With 45,000+ production‑grade MSTX/XML rows delivered, the engagement demonstrates that AI‑powered modernization can meet and exceed enterprise tax compliance standards—reliably, consistently, and at scale.

Beyond immediate operational acceleration, the solution established a long‑term modernization foundation:

• • A future‑ready tax calculation platform
  • Reduced risk through automated, consistent validation
  • Faster onboarding of new jurisdictions and tax updates
  • Permanent capture of institutional tax‑logic expertise
  • Significant cost and time savings for ongoing modernization efforts