The wave of AI-driven code modernization tools has arrived with dramatic force. From discovery to code generation, AI is meaningfully changing the cost-benefit equation of mainframe modernization for enterprises. What once required months of manual analysis can now be accelerated dramatically; tests can be derived quickly, helping generate largely functional new code.

That progress matters. Discovery in particular has historically consumed a disproportionate share of modernization budgets. When AI reduces that effort, systems that were previously too expensive to analyze now become viable candidates for modernization. Entire portfolios move from “untouchable” to “worth considering” now that the cost calculus has improved.

And while we, at Mechanical Orchard, use LLMs extensively in our work, we are also tackling the less popular and stickier problem: how to reduce the risks associated with mainframe modernization. These risks can include extended cutovers that disrupt operations, mismatched business logic that impact pricing, integration breakdowns across downstream systems, and the loss of institutional knowledge embedded in decades of production behavior.

So while lowering the cost of modernization is an important step forward, lowering the risk of modernization requires something more deliberate: an approach designed around exhaustive validation to create an exact replica first.
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Why code isn’t enough

COBOL is only one visible layer of a much deeper system. Mainframe environments are built on tightly integrated stacks that include transaction managers like CICS, databases such as IMS or DB2, batch schedulers, messaging layers, security frameworks, and decades of incremental adjustments made in production. The source code reflects business logic, but it does not fully capture the operational assumptions that have accumulated around it.

Consider two large banks running similar core banking platforms. On paper, their architectures may look nearly identical. Both may process deposits, loans, payments, and reporting through comparable COBOL programs. Yet over the years, each institution has adapted its systems differently. One may have introduced performance optimizations to handle peak transaction volumes during specific market cycles. Another may have embedded compliance-related controls shaped by regulatory audits. Batch job timing, data conventions, integration contracts with downstream systems — all of these details evolve in response to local pressures.

Those differences rarely announce themselves clearly in the codebase. They reveal themselves in how the system behaves in production. Focusing solely on the COBOL creates the illusion that the system is fully understood when, in reality, much of its behavior remains implicit.

When modernization efforts concentrate on code translation, they risk perpetuating this blind spot. Translating syntax does not guarantee equivalence of behavior. After all, modernization is not greenfield development; it is the reimplementation of a mission-critical system that already works. Subtle behavioral drift can create measurable financial discrepancies, compliance violations, or customer-facing disruptions. These are not theoretical risks; they translate directly into quantifiable losses.
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Trust, but verify

Addressing risk requires a different approach not focused on code. Instead of beginning with translation, we capture how the system actually behaves in production and use that data to verify generated code so that it faithfully reproduces the legacy workload before it replaces it.

These exact behavioral replicas of legacy systems reproduce their functional outputs, performance characteristics, and integrations in a modern environment. AI plays a role throughout - we have been pushing the limits of LLMs - and the defining step is validating the new system against the real behaviors of the old one. By proving behavioral equivalence first, modernization can proceed incrementally and with materially lower risk.

AI has fundamentally changed the economics of modernization. It makes analysis faster, translation cheaper, and previously inaccessible systems newly approachable. But modernization cannot simply be a code conversion exercise; it should start with a careful, exact re-creation of the living, critical system. That requires proving behavioral equivalence before replacement — validating not just what the code says, but what the system actually does. AI expands what is possible. A behavior-first approach determines what is safe.

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