G.Tx Transform - modernize legacy code 5x faster with agentic AI

No — and that distinction is the point. A prompt is a single instruction; G.Tx runs governed workflows: repeatable sequences with structured inputs, validated outputs, and traceable evidence. Prompts produce snippets; workflows produce changes a CTO can defend in a steering committee. Reliability is a property of the workflow surrounding the agent, not of the agent alone.

Deliberately hiding the integration tests from the component generator prevents a common failure mode — output shaped to pass a specific set of tests rather than to implement the feature correctly. The generator works from the feature description and a contract; a separate functional evaluation step is the only place the tests are run against the result.

Straightforward, high-volume cases go through pre-defined G.Tx Workflows Templates — proven, repeatable transformation patterns. Complex tasks go through an agentic coding tool (e.g. Claude Code) working from specs and agentic search. Both paths converge on the same code review gate before anything reaches the repository.

Two evaluations run automatically: an LLM evaluation that scores each step’s output against its contract, and a functional evaluation that runs the integration tests against the new component. Engineers approve the final transformation output — the component and the evidence — and when an evaluation fails, they refine the step that produced it. Human-in-the-loop validation stays mandatory; it’s targeted at the output, not every prompt.

A single agent driving work end-to-end runs out of context and judgment beyond one feature. G.Tx scales differently: because each step is named, evaluated, and approvable on its own, the same workflow shape applies whether the target is one feature or the whole system, with bulk file processing across wider codebases. Each pilot reuses the contract patterns, tests, and workflows from the last, accumulating into a working modern subsystem.

Before any code is generated, integration tests are authored against the legacy feature’s actual behaviour, reviewed and refined, and then treated as the behavioural standard. This is test-driven development applied to modernization: the tests come first, the new code is written against them, and the same tests judge whether the result is equivalent. Nothing downstream begins until those tests are approved.

By default, no. The new component is shaped by the description of the feature, not by the patterns of the original developers or a mechanical translator. Legacy source is provided only where a specific integration must be preserved exactly — for example a SQL stored procedure or an external API the new component must call in its original form.

Every step has a narrow, named job and a reviewable output, and every result is traceable — you can show what produced a component, what evaluated it, which tests it passed, and who signed off. Generated code is stored with full traceability in the repo. Engineers don’t review every intermediate diff; they approve the transformation output plus the evidence it satisfies the integration tests.

Yes. G.Tx generates agent-ready specifications compatible with tools like Claude Code and Codex, and supports integration with any external custom agent. It’s designed to work alongside your existing AI development tooling rather than replace it.

The Transformation Pilot takes one scoped unit through Design, Build, and Run end-to-end — a focused engagement, not a system-wide commitment. It proves the approach on real code and produces a modernized component in production plus a repeatable process, before full-scale investment. Rollout follows an incremental strategy (feature flags, canaries, gradual traffic shifting, observation periods) so the legacy system keeps running while modernized components are cut over.