· Generative AI · 2 min read
Accelerating Legacy Modernisation with GenAI Agents
Refactoring monoliths to microservices is risky and slow. Discover how specialised AI Agents can de-risk migration, automate test generation, and modernise your legacy stack.
Legacy systems are the silent killers of innovation. We have seen it time and again: a major bank wants to launch a mobile-first feature, but their core banking engine is stuck in 1980s COBOL spaghetti code.
Traditionally, modernisation programmes are multi-year, multi-million pound nightmares. But Generative AI Agents are changing the calculus.
The GenAI Advantage in Migration
Startups and agile enterprises are moving away from manual “lift and shift” towards AI-assisted refactoring. This isn’t just about asking ChatGPT to “rewrite this function”. It is about deploying specialised agents that understand the entire dependency graph of your application.
1. Automated Test Generation (The Safety Net)
The biggest fear in touching legacy code is breaking something. Often, the original developers retired years ago, and there are zero unit tests.
GenAI agents can analyse the legacy code execution paths and generate comprehensive unit tests (e.g., JUnit or PyTest) that characterise the current behaviour. This creates a safety net, ensuring that the modernised version behaves exactly like the original.
2. Intelligent Code Translation
While simple transpilers exist, they often produce unreadable code. GenAI models, fine-tuned on code, can translate languages (e.g., Java 8 to Kotlin, or Mainframe to Node.js) while preserving idiomatic patterns.
We recently helped a logistics client migrate a monolithic PL/SQL codebase to Python on Google Cloud. The AI agents handled 80% of the repetitive translation, leaving the senior engineers to focus on architectural optimisation.
3. Documentation from the Crypt
One of the most valuable capabilities of GenAI is its ability to “read” code and explain it.
By running an “Explainer Agent” over your legacy repository, you can generate fresh, accurate documentation. This allows new hires to understand business logic that was previously locked inside a binary.
Challenges to Watch For
It is not magic. There are risks:
- Hallucinations: The AI might invent a library that doesn’t exist. Strict compilation checks are mandatory.
- Security: You must ensure you are not sending proprietary code to a public model without a contract.
Conclusion
Modernisation is no longer a five-year death march. With the right AI strategy, you can decompose your monoliths in months, not years.
At Alps Agility, we specialise in deploying secure, enterprise-grade AI agents for code migration. We help you de-risk your modernisation journey. Contact us today to audit your legacy stack and build a roadmap for the future.
