How to modernize legacy codebases using AI coding agents (Step by step guide)

Summary

Modernizing legacy codebases often presents severe challenges, including missing documentation, complicated data flows, and an absence of automated tests. Rather than trying to understand an entire system at once, developers can adopt a change request driven approach that modernizes code incrementally as specific requirements arise. Because AI coding agents excel at tracing data paths from user interfaces through APIs down to databases, they can update interdependent code paths across a system when a single field or feature changes. This approach organizes legacy modernization into three main phases: planning, execution, and verification.

The planning stage centers on preparing a safe development environment and mapping dependencies before writing code. Developers should document the local setup, including compilers, linters, and test runners, in a central README file. This documentation allows the agent to execute commands autonomously, catch syntax errors, and iterate in a tight feedback loop. Encapsulating this workflow in a Docker container ensures the environment remains disposable and easy to reset. In the Antigravity IDE or CLI, developers engage planning mode to instruct the agent to inspect the codebase and identify every file, API endpoint, or database table that could be affected by a change. Rather than immediately generating code, the developer collaborates with the agent by requesting multiple architectural designs, critiquing plans, and prompting the agent to ask clarifying questions about overlooked requirements.

The execution stage transforms these plans into code while strictly managing the agent's context window. To prevent the agent from becoming confused during extended sessions, large features are divided into distinct subtasks tracked in a markdown checklist. After completing each task, the agent summarizes its changes and carries that context into the next step. To maintain software quality, developers apply test-driven and behavior-driven development. Advanced reasoning models like Gemini Pro analyze tickets to define acceptance criteria and write failing tests, after which higher-throughput models like Gemini Flash write the minimum code needed to pass the tests. Defining strict agent rules ensures the AI respects formatting standards, runs builds, and never commits code without human approval.

The verification stage establishes safety barriers to keep model hallucinations and regressions out of production environments. This step applies a three-layer filter. First, human engineers perform manual diff reviews to guarantee that new code remains readable and maintainable for both future developers and AI tools. Second, the CI/CD pipeline uses a secondary AI model to perform code reviews on pull requests, requiring the initial authoring agent to justify its implementation decisions. Third, automated deployments send code to a staging environment where developers conduct smoke tests to ensure runtime stability.