Map the code.
Explore dependencies, interfaces and important workflows. Check the generated explanation against running behaviour and the people who use the system.
AI-assisted modernisation
Understand the old system.Change it carefully.
Use AI to investigate an existing codebase and prepare focused changes. Keep architecture, review and release decisions with experienced engineers.
Explore the capabilityENGINEERING FOCUS
01Understand the codebase
02Test a focused change
03Engineer-reviewed release
Start by finding out how it works.
Before rewriting a system, understand its dependencies, behaviour and business constraints. AI can assist the investigation; it does not remove the need to validate what it finds.
Build a testable baseline.
Capture existing behaviour and identify gaps. Test changes in an isolated branch or environment, with secrets and production access restricted.
Review and release.
Inspect generated code, test security and regressions, and deploy in controlled increments. Keep a practical rollback route.
A possible workflowIllustrative example.
Investigate an undocumented integration, add regression tests and prepare a narrow change for review.
Generated code can be plausible and wrong. Faster output is not a substitute for a correct change or a maintainable system.
A closer look
Good questions.
Straight answers.
Does this mean replacing the development team?
No. It gives engineers another tool for investigation and implementation. The engineering decisions and accountability remain with people.
Should we rewrite the entire system?
Only with a clear reason. Improving interfaces, tests or a single troublesome component may be more valuable and less disruptive.
Technical reference: OpenAI: Codex workflows (opens in a new tab)
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