Legacy Codebase Modernization Using Dynamic Workflows

Claude Code Opus 4.8 uses dynamic workflows and Git worktrees to convert legacy codebases from frameworks like jQuery, AngularJS, or Python 2 to modern equivalents such as React, Next.js, or Python 3. The architecture agent performs a full codebase scan to build a dependency graph in DOT format, a module boundary map with ownership assignments, and a type inference model that documents implicit type contracts across modules. It then creates parallel Git worktree branches where teams of subagents analyze, rewrite, and test individual modules concurrently. Each worktree runs its own build and test suite independent of the others, so twenty modules can be converted in parallel instead of sequentially. The agentic reasoning step involves the architecture agent polling subagents for their interface definitions, detecting incompatible signatures between modules being converted simultaneously, and issuing typed interface correction specs that affected subagents apply before attempting merge. Measurable outcome: 50,000 lines of legacy code ported in 5-7 days with 95% test coverage and zero regression in end-to-end test suites.

BUSINESS PROBLEM

Companies like CRED maintain millions of lines of legacy JavaScript and Python written before modern frameworks existed, held together by outdated build tooling and undocumented workarounds. Rewriting manually costs $500,000 to $2,000,000 and takes 6 to 18 months for a typical mid-size application with ten interdependent modules. During that time, the product team cannot ship new features because all engineering bandwidth is consumed by the rewrite effort. [ STAT ] 85% of enterprise organizations cite legacy modernization as a top-three IT risk, with average projects running 45% over budget and 60% over schedule. — McKinsey Digital Transformation Survey, 2024. Teams cannot halt feature development for months just to rewrite, so legacy code accumulates unpatched security vulnerabilities and slows every new feature addition by 3 to 5 times compared to implementing the same feature in a modern framework. The technical debt compounds: each month of delay means more code gets written in the legacy framework that must also be converted later, increasing the total conversion scope.

WHO BENEFITS

1. Engineering leads at CRED who manage a 200,000-line AngularJS payment dashboard that must be fully ported to React before the AngularJS long-term support window ends, with zero downtime for 10 million monthly transacting users and no regression in payment success rate or average API response time during the cutover. 2. Backend platform engineers at Spotify who maintain Python 2.7 data processing pipelines that are now locked out of using any PyPI package published after 2020 and need a line-by-line accurate conversion to Python 3.12 that preserves identical numerical output for every historical data file across all batch processing runs spanning three years of archived data. 3. CTOs at Series B fintech startups who inherited a jQuery monolith built by a third-party contracting firm with no documentation, no test suite, and no deployment runbook, and need a predictable timeline with a fixed cost bound for full modernization before a Series C technical due diligence audit uncovers the maintenance risk and depresses company valuation.

HOW IT WORKS

1. [TOOL: Claude Code Opus 4.8] Architecture scan: principal agent reads every file in the legacy codebase, builds a dependency graph in DOT format, and identifies module boundaries, external API contracts, and type signatures. 2. [TOOL: CLAUDE.md architecture specs] Conversion plan: agent writes a CLAUDE.md file specifying the target framework patterns, naming conventions, import style, and test framework for the new codebase. 3. [TOOL: Git worktrees] Branch creation: agent creates one Git worktree per module (8-12 worktrees), each checked out to an independent branch with its own build tooling and test runner configured. 4. [TOOL: Claude Code Subagents] Parallel conversion: each subagent in its worktree reads the legacy module, rewrites files to the target framework, and runs the module's test suite. Failed tests trigger automatic re-attempt with context from error logs. 5. AI Reasoning: principal agent polls subagent results, detects interface mismatches between modules being converted in parallel, and issues interface correction specs that affected subagents apply before merge. 6. [TOOL: Claude Code Opus 4.8] Integration testing: all worktrees are merged into a staging branch. The agent runs the full end-to-end test suite and compares output against the legacy baseline for bitwise equivalence on data transforms. 7. Human Review: module leads review the diff between legacy and modern code for three modules per day, focusing on business logic correctness. They approve or request re-conversion with specific notes. 8. [TOOL: CLAUDE.md memory] Finalization: agent updates CLAUDE.md with architecture decisions made during conversion, rationale for framework choices, and migration notes for the operations team.

TOOL INTEGRATION

Claude Code Opus 4.8: Use the --max-context flag at its highest setting for the architecture scan, then switch to per-module context windows for conversion subagents. The architecture scan step costs $10-30 in API usage for a 50,000-line codebase. Gotcha: The architecture scan produces a dependency graph that can exceed the output token limit if the codebase has more than 500 files. Configure the agent to generate the graph incrementally, one directory level at a time, and merge results in a separate step. Git worktrees: git worktree add creates independent working directories sharing the same Git metadata. Each worktree runs on its own branch, so parallel agents do not conflict. Gotcha: Subagents in different worktrees must not run git gc or git prune as these can corrupt shared object references. Disable automatic garbage collection in worktree setup scripts. Subagent orchestration via dynamic workflows: The dynamic workflows JavaScript loads the module list from the dependency graph JSON and spawns one agent per module using Agent.create(). Each agent receives the legacy file path, target framework spec, and a CLAUDE.md subset. Agents communicate results back via file system writes to a shared status directory. Gotcha: If more than 12 worktrees are created, file system watchers on macOS may fail silently due to the system-level file descriptor limit. Raise the limit with launchctl limit maxfiles before starting the workflow. CLAUDE.md architecture specs: Write target framework rules explicitly including import patterns (e.g., named exports preferred), error boundary requirements, and state management patterns. Gotcha: If specs are too permissive, subagents produce inconsistent code styles across modules. Include at least 10 concrete pattern examples in CLAUDE.md to constrain output.

ROI METRICS

1. Lines of legacy code converted per day per engineer: Before 200 to 400 lines with manual rewrite and hand-testing → After 7,000 to 12,000 lines with agent-assisted conversion and automated test generation. 2. Total project duration for a 50,000-line legacy codebase: Before 4 to 6 months of full-time engineering → After 5 to 7 days of agent execution with human review. 3. Test coverage on the converted codebase at ship time: Before 20% to 40% on legacy code (manual tests only) → After 92% to 97% including auto-generated unit and integration tests. 4. Post-migration regression incident rate per quarter: Before 5% to 15% of production incidents traced to migration errors in manual rewrites → After 0% to 2% of incidents. 5. Engineering hours spent on migration supervision per week: Before 40+ hours doing the conversion → After 10 to 15 hours reviewing agent output and handling edge cases.

CAVEATS

1. Dependency graph incompleteness: The architecture scan may miss implicit dependencies such as dynamic imports, eval-based module loading, or Ruby monkey-patches. These surface as runtime errors during integration testing and require manual patching. 2. Framework-specific idioms not mapped: The CLAUDE.md conversion rules may not cover every legacy framework pattern, especially jQuery plugin chains and AngularJS two-way binding edge cases. These modules require manual conversion or custom rule addition. 3. Worktree resource contention: Each worktree runs its own build process and test runner. On a single developer machine, 8-12 parallel builds can exhaust RAM and swap, causing subagents to timeout. Use a CI runner with 32GB+ RAM or distribute worktrees across machines. 4. Business logic misinterpretation: The agent may incorrectly preserve legacy bug behavior as correct logic when the legacy test suite is also buggy. Human reviewers must verify business logic against product requirements, not just against legacy test output.