Practical Example of Reworking Legacy Code Base Using Solely Orchestrated Agents

by Nicolas Rubliauskas Umaras, Principal Software Developer

The Real Cost of Legacy Debt Isn’t Technical

Undocumented business logic. Missing test coverage. Tightly coupled components that turn every change into a liability. These problems aren’t new — they’ve been accumulating for years. What’s changed is the cost of standing still.

In environments where speed of iteration is a direct competitive differentiator, a fragile legacy system isn’t just a maintenance burden. It’s a strategic constraint — one that limits what your engineering teams can build, how fast they can ship, and how confidently they can deploy.

Traditional refactoring doesn’t solve this. It slows teams down further, demands that engineers internalize complexity that shouldn’t require human memory to hold, and produces timelines that organizations consistently underestimate. Six weeks becomes six months. Six months becomes never. Missing documentation becomes the final nail in the coffin as the new version introduces more bugs than it resolves.

Orchestrated AI Agents Change the Execution Model Entirely

The bottleneck in legacy modernization has never been intent. It’s been execution bandwidth.

Understanding a complex system, extracting its implicit logic, faithfully translating it into a modern architecture, and validating the result, all at once, requires more parallel cognitive effort than human teams can sustain at speed. Engineers get fatigued. Consistency degrades. Edge cases get missed. The work slows down precisely when it needs to accelerate.

Orchestrated AI agents are engineered exactly for this. Not as assistants to human engineers, but as parallel execution layers operating under deliberate engineering oversight.

In a structured agentic workflow, specialized agents handle codebase mapping, business logic extraction, code translation, and behavioral consistency validation simultaneously. The orchestration layer ensures agents operate cohesively, outputs are continuously cross-validated, and errors are caught before they propagate across the stack.

This isn’t a workflow enhancement. It’s a different execution model.

What This Looks Like When It’s Running

A legacy application (Python backend, fragmented HTML, scattered JavaScript) needed to become a unified, modern TypeScript system. No surviving documentation. No original architects available. Mission-critical functionality that could not be lost in translation.

Traditional estimate: six weeks, under optimistic assumptions.

Agentic execution: 90% feature parity in two weeks.

The agents didn’t just translate code. They surfaced the original system’s structural weaknesses to make better architectural decisions in the rebuild. The result wasn’t a like-for-like migration. It was a materially better system: clear module boundaries, strong typing, improved security posture, a redesigned interface, and an asynchronous processing layer built on RabbitMQ. What began as a modernization effort became a foundation for entirely new products.

The Division of Labor Is Deliberate

Speed without correctness isn’t an outcome; it’s a liability. The model works because it’s built around a precise boundary between what agents own and what engineers own.

Agents handle the high-volume, well-defined work: structural mapping, documentation generation, language translation, test generation, dead code detection, behavioral equivalence checks. The tasks where human engineers accumulate fatigue and introduce variance.

Engineers own the decisions that require architectural reasoning: module boundaries, equivalence criteria, edge cases, ambiguous logic. That’s not a concession to AI’s limitations. It’s the deliberate design of a system built to produce outputs you can actually ship.

We’re Already Running This

Sigma Software’s AI-native engineering teams don’t test these approaches in controlled scenarios — we run them on real projects, for real clients, every day. We have a dedicated group of engineers advancing this framework continuously, navigating the agentic frontier and turning that R&D directly into client outcomes.

The organizations pulling ahead aren’t the ones with the most sophisticated AI roadmaps. They’re the ones that have already moved, compounding execution speed, while others are still scoping the project.

If your modernization timeline is measured in quarters, we can show you what weeks look like.

• #AI/ML
• #Migration & Integration