Legacy software migrations used to be the kind of project that kept CTOs up at night. But AI agents are starting to take on work that once required months and expensive specialists - and it's worth understanding what's actually happening.
The Real Cost of Living With Old Code
If you've ever worked near a software team managing a legacy Java codebase, you know the tension. The application works - sort of. But it runs on an older framework version that the vendor no longer fully supports. Security patches are slow to arrive. New developers struggle to onboard because the patterns are outdated. And every time someone suggests migrating to a modern framework version, the room goes quiet.
The hesitation is understandable. A typical enterprise Java migration - say, moving from an older version of Spring Boot to a current one, or shifting from a legacy Jakarta EE setup - involves hundreds or thousands of files. Dependency trees have to be untangled. APIs that changed between versions need to be mapped and rewritten. Tests have to be updated. It's painstaking, detail-oriented work that traditionally required experienced engineers who knew both the old and new frameworks deeply.
For small businesses or lean product teams, this kind of project often just gets deferred indefinitely. The cost in time and money is too high relative to other priorities. So the technical debt compounds, quietly making every future feature harder to build.
What AI Agents Actually Do Differently
There's an important distinction worth drawing here between an AI chatbot and an AI agent. When you use a chatbot to help with code, you're having a conversation - you paste in a snippet, it suggests a fix, you apply it manually and move on. Useful, but still mostly you doing the work.
An AI agent is different. It can take a goal, break it into subtasks, execute those subtasks across multiple files and systems, check its own work, and iterate - often without needing you to hold its hand at every step. Applied to a codebase migration, that means an agent can scan the entire project, identify deprecated API calls, apply the correct replacements according to the new framework's documentation, update configuration files, and run tests to verify nothing broke - all in sequence.
What makes this newly viable is a combination of factors: better reasoning in large language models, improved tool-use capabilities that let agents interact with file systems and code execution environments, and growing investment in benchmarking frameworks that measure how well agents actually perform on real migration tasks. Researchers and engineering teams are now building structured evaluations - essentially standardized test suites - so we can measure whether an agent truly migrated code correctly, not just whether it looks plausible on the surface.
Real Example - Step by Step
Let's say you're a product manager at a mid-sized company. Your engineering team maintains a Spring Boot 2.x application that needs to move to Spring Boot 3.x to maintain security compliance. There are roughly 400 Java files involved. Your lead developer estimates it's a 6-to-8-week project done manually, and the team is already stretched.
Here's roughly how an AI agent-assisted migration plays out in practice:
Step 1 - Discovery. The agent is pointed at the repository. It scans every file, catalogs all dependencies listed in the build file (Maven or Gradle), and flags every API call or annotation that changed between versions. This produces a migration map.
Step 2 - Planning. The agent ranks changes by complexity - straightforward dependency version bumps versus more involved refactors where the underlying API signature changed significantly. It creates a work queue.
Step 3 - Execution. Starting with the lower-risk changes, the agent rewrites code file by file. It handles things like namespace changes (a significant one in Spring Boot 3.x was the move from javax to jakarta packages), updated annotation patterns, and deprecated method replacements.
Step 4 - Verification. The agent runs the existing test suite after each batch of changes. Where tests fail, it examines the failure, determines whether the issue is in the migration logic or in the test itself, and attempts a fix.
Step 5 - Handoff. The agent produces a summary report: what was changed, what it couldn't resolve automatically, and what needs human review. Your engineer reviews the flagged items - a much smaller task than doing the whole migration from scratch.
What took weeks manually might realistically take a long weekend of compute time, with your engineer spending a day reviewing edge cases rather than doing the bulk migration themselves.
How to Apply This Today
You don't need to be a developer to start thinking about this strategically. Here's what you can do right now:
If you're evaluating vendors or tools: Look for solutions that can show you benchmark results on standardized migration tasks - not just demos on clean toy examples. The quality gap between agents is significant, and real evaluation data matters.
Start small. Even a modest pilot - migrating one module or one service - gives your team confidence in the process and surfaces the edge cases before you commit to something larger.
Key Takeaways
Legacy migration projects are a major source of deferred technical debt, especially for smaller teams
AI agents differ from chatbots by executing multi-step tasks autonomously across entire codebases
Benchmarking frameworks are emerging to measure agent migration quality on real, standardized tasks
A human engineer's role shifts from doing the migration to reviewing the agent's output - a much faster process
Even non-technical stakeholders can drive this conversation by asking the right questions of their engineering teams
What's your experience with this? Drop a comment below - I read every one.
Sources referenced: ScarfBench: Benchmarking AI Agents for Enterprise Java Framework Migration - Hugging Face Blog
