Citing:
“Everyone is talking about using AI to migrate Oracle Forms. The pitch is attractive: “Point an AI agent at your FMB files. Get a modern application out.”

Here is the part that does not make it into the pitch: Oracle Forms is not a screen-based system. It is a logic engine. Hundreds of triggers. Interdependent program units. Event-driven state tightly coupled to PL/SQL and runtime behavior.

When an AI agent infers what a trigger “probably means” instead of transforming it deterministically — it introduces silent logic drift...”*

*Source. If You want read more: https://www.linkedin.com/posts/renaps_why-agentic-ai-tools-dramatically-increase-activity-7457863008912896001-Xpqn/

RF21′ comment:

The current radical criticism of autonomous AI tools in legacy modernization processes targets a false adversary, diverting attention from actual failures in architectural governance. The assertion that AI agents drastically increase the risk of losing systemic control is valid in only a single scenario: when artificial intelligence is forced to ‘guess’ the intent of legacy developers based solely on raw, isolated source code. Introducing a deterministic framework in the form of a comprehensive system dependency graph (RIB) completely changes the game, transforming the speculative hypotheses of generic models into precise engineering operations. Transformation security does not stem from restricting automation or returning to manual code rewriting, but from deploying continuous, automated runtime proof of correctness (RTA) that immediately validates business process execution. Rejecting the potential of autonomous agents due to a fear of their unpredictability constitutes a technical capitulation; market leaders will be those organizations that successfully harness the probabilistic nature of LLMs through rigorous structural verification tools.

The enterprise sector is beginning to resemble a technological casino, where the stakes are the stability of mission-critical business systems.
To put it bluntly and without excessive diplomacy: deploying generic AI agents for legacy system migrations without a strict, engineering-grade leash is a near-certain technological suicide mission.
Why? Because today’s LLMs are brilliant illusionists. They generate code that looks fantastic—maintaining structure, featuring flawless syntax, and capable of passing any superficial code review (producing so-called “plausible code”). However, in mission-critical enterprise architectures (such as core banking, insurance, or logistics systems), code must be 100% correct, not merely “probable.” When you migrate complex, rigorously audited business logic (e.g., embedded within legacy PL/SQL packages or Oracle Forms), the AI model attempts to guess the intent of a developer from twenty years ago. The result is a ticking time bomb with a delayed ignition, manifesting as hidden runtime errors and corrupted transactions.
Where lies the real controversy of this debate?
Most traditional vendors and systems integrators stop at mere AI fear-mongering. Why? Because they are terrified of losing their massive, multi-million dollar budgets allocated for manual code rewriting and endless billable hours. On the other end of the spectrum, radical AI evangelists promise core system migrations via a single prompt, which is pure fantasy. Both groups are profoundly mistaken and are wasting clients’ time and capital.
Artificial intelligence technology itself is not inherently flawed. The problem lies in how mindlessly we attempt to feed it. Submitting “raw” legacy code to an LLM and expecting a flawless, native application is absurd. AI lacks visibility into the multi-layered dependency map, the underlying architecture, and the system’s metadata.
From the perspective of Reforms21, our position is radically pragmatic: secure, AI-driven migration automation is possible, but it requires a complete paradigm shift. Instead of a probabilistic approach (guessing), artificial intelligence must be confined within a framework of deterministic analysis:
AI has no right to guess: AI assistants must be driven and rigorously constrained by a structural system graph and a precise dependency map of the legacy code. Models must operate on hard architectural facts, not hallucinations.
Blast radius control: Without combining static analysis with the automated generation of regression tests directly at the runtime level, every AI-generated code compilation is a game of Russian roulette. You must possess mathematical certainty regarding how a modification in one location impacts the rest of the ecosystem.
Therefore, instead of practicing a corporate religion around Agentic AI or, conversely, falling into Luddism and frantically rejecting innovation, it is time for hard engineering. AI is a powerful engine, but without a precise chassis composed of rigorous automation and code-mapping tools, it will not take us very far. Enterprise clients are not sold on “the hope of working code”; they require a guarantee of absolute business consistency.