AI Agents Cut Legacy Code Modernization Times by 53 Percent

Enterprise software development is shifting its focus from generating new code to excavating and understanding legacy systems. In a recent project led by IT engineering firm Coditation, developers used AI agents to modernize a battery distribution company running more than 15 legacy applications. Instead of immediately rewriting the software, the agents mapped the connections between applications and surfaced undocumented business logic. This approach reduced the project timeline from an estimated eight and a half months to just four months, representing a 53 percent reduction in delivery time.

This efficiency gain addresses a massive financial drain for global businesses. A 2025 study by Pegasystems and research firm Savanta, which surveyed over 500 IT decision-makers worldwide, found that enterprises waste an average of more than $370 million annually due to difficulties in modernizing legacy systems. Nearly $134 million of that waste is directly tied to slow, resource-intensive transformation projects. Rather than a lack of developer talent, these organizations suffer from a shortage of institutional memory when original programmers leave.

To solve this, practitioners are using AI as an analytical tool rather than an automated writer. This methodology aligns with Anthropic's official guidance on using its Claude Code tool to modernize legacy COBOL systems, which emphasizes automating exploration and analysis before attempting any code translation. The recommended workflow follows a strict sequence: discover system connections, understand the underlying business logic, verify those assumptions against real production logs, and only then transform or migrate the code with human sign-off.

For software engineers, this shift redefines the role of AI from a simple code generator to an organizational archaeologist. However, experts warn against granting AI agents full autonomy over critical business logic, as unverified assumptions in legacy systems can cause catastrophic failures. Furthermore, a 2026 multivocal literature review on LLM-assisted development warns that rushing to write new code with AI is already creating "fast-integration debt," meaning today's rapid deployments will eventually require the same archaeological excavation.