Nobody sets out to build a legacy system. They begin as well-designed solutions to real problems. They work so well they become critical infrastructure. Then time passes, requirements accumulate, developers rotate, and one hasty fix at a time the system becomes something nobody fully understands anymore.

We (senior developers) all know the story. The outdated library that’s too risky to upgrade, the undocumented script discovered on a production server. Bugs that only manifest on Tuesdays for accounts created after February. When it finally becomes clear the system needs to be rewritten, estimates come in: six figures, multi-year timelines, full teams deployed.

I spent the last months exploring agentic AI applications but so far the most practical value I’ve found is simpler: Claude Code is exceptionally good at exactly those tasks that make legacy modernization so painful for humans.

Dependency archaeology

Legacy systems accumulate libraries that are outdated, discontinued, or simply vanished from the internet. Claude Code can scan the codebase, identify the worst offenders, and suggest modern replacements. Trivial dependencies can be written from scratch, eliminating the external dependency entirely.

Reverse engineering at scale

A decade-old system often contains hundreds of thousands of lines written by dozens of developers, most of whom left no documentation. It takes months for a human engineer to understand this code well enough to safely modify it. Claude Code generates readable summaries and traces logic flows tirelessly, producing in hours what would take weeks of careful reading.

Dead code identification

Legacy codebases bloat because deletion is risky. New features get added but old code rarely gets removed. Cross-referenced with production logs, Claude Code identifies what’s actually executing versus what’s just taking up space.

Exposing fossilized hacks

Quick fixes have a way of becoming permanent fixtures. It’s not unusual for a legacy app to be entangled with its original dev environment: hard-coded scripts, file-system paths set years ago that have since cemented into load-bearing parts of the application. These are notoriously difficult for humans to trace but CLI-driven agent coders excel at exactly this kind of detective work.

Generating a preliminary spec

Perhaps the most valuable output is turning the existing application into a readable specification with minimal human effort. Claude Code extracts what the system actually does, how components interact, and which business rules are embedded in the logic. The result is a solid foundation for a proper spec that humans can review and refine rather than having to create from scratch. For systems where the original requirements are long lost or hopelessly outdated, this alone can save months.

Conclusion

This changes the economics dramatically. Detective work that previously took months of senior developer time can happen in days. The system that seemed untouchable becomes approachable. One capable developer armed with sound Claude Code skills can get the work well under way without having to deploy a full team.

This isn’t speculative. Last week I analyzed an Android app repository I had not seen before and fixed an old issue. The pull request was approved the same afternoon. This would’ve been impossible a year ago, which goes to show how fast the tools are developing and how good they’ve already become.