Legacy Code Migration Is the Killer App for AI Agents — And It's a $100B Problem
Devin's context window just crossed 10 million tokens — enough to understand an entire enterprise codebase in a single session. Cognition reports that legacy code migration (COBOL to Python, Fortran to Rust, monolith to microservices) is now their fastest-growing enterprise use case, with Nubank achieving 8x efficiency gains on large-scale refactors.
The timing matters. IDC estimates enterprises spend over $100 billion annually maintaining legacy systems — patching COBOL mainframes, nursing decade-old Java monoliths, and paying premium rates for developers who understand dying languages. Deloitte's 2026 agentic AI strategy report confirms that code modernization is the #1 enterprise driver for autonomous agent adoption, ahead of even greenfield development.
Why agents excel here: legacy migration is the perfect intersection of tedious, high-volume, and pattern-rich — exactly where AI agents outperform humans by the widest margin. A developer migrating COBOL to Python reads thousands of lines, understands business logic buried in 30-year-old comments, maps data structures, writes equivalent modern code, and tests it. An agent team does the same thing in parallel across the entire codebase, 24/7, at a fraction of the cost.
But here's what Cognition's case studies reveal: single-agent migration fails on real codebases. The successful deployments — Nubank, Infosys across banking and payments, Cognizant's global operations — all use orchestrated multi-agent systems. One agent analyzes the legacy code. Another generates the modern equivalent. A third writes tests. A fourth validates business logic preservation. A human engineer reviews critical decision points. It's not one smart agent — it's a coordinated migration team.
The Anthropic 2026 Agentic Coding Report backs this up: agents now handle "complex multi-step tasks that were impossible 18 months ago," but production success requires orchestration, not just capability. For enterprises sitting on legacy systems, the economics have flipped. The cost of maintaining COBOL mainframes now exceeds the cost of AI-driven migration. And with agent context windows large enough to understand entire repositories, the technical barriers have collapsed too.