What's actually breaking when companies try to use AI agents for legacy code modernization?

Been working in this space for a while now, helping teams modernize old codebases (COBOL, legacy Java, that kind of thing) using AI agents for things like requirement extraction, code generation, and test automation. A few patterns I keep running into that don't get talked about much: Most AI codegen tools are great at greenfield code but fall apart on legacy systems because there's no clean documentation or requirements to work from Test automation agents often generate tests that pass but don't actually validate the right business logic, because the original intent was never written down anywhere Nobody talks about the requirements gap, most legacy systems were never properly documented, so any AI agent working on them is guessing at intent, not just syntax Curious what others here are seeing. Anyone using agents for legacy modernization specifically, not just new code? What's actually working vs what's hype?