Refactoring COBOL to Java with Agentic AI with an Iterative Refinement Workflow скачать в хорошем качестве

Refactoring COBOL to Java with Agentic AI with an Iterative Refinement Workflow

Legacy COBOL systems still run critical business infrastructure—but migrating them safely to modern languages is hard, expensive, and risky. In this webinar, OpenHands senior researcher Calvin Smith walks through a practical iterative refinement workflow where one agent refactors COBOL → Java and a second “critic” agent scores the output and generates actionable feedback—looping until it meets a quality threshold. Links: Presentation: https://ggl.link/coboltojavawebinar Blog (COBOL → Java, iterative refinement): https://openhands.dev/blog/20251218-c... OpenHands Software Agent SDK (GitHub): https://github.com/OpenHands/software... SDK Docs: https://docs.openhands.dev/sdk OpenHands Website: https://openhands.dev/ Contact / Get in touch: https://openhands.dev/contact OpenHands GitHub org: https://github.com/OpenHANDS Presentation: 00:00 – Welcome & introductions 01:16 – Why COBOL modernization is urgent 02:12 – The real risks of legacy COBOL systems 03:25 – Why COBOL → Java migrations fail 05:02 – Why AI agents struggle with large refactors 06:26 – The core idea: Iterative refinement with AI agents 08:49 – Engineer ↔ Critic agent workflow explained 10:40 – Why this approach works (feedback loops & convergence) 13:16 – Proof of concept results & what was achieved 14:19 – OpenHands Agent SDK overview 17:21 – Tip #1: Traceability from Java back to COBOL 19:17 – Tip #2: Concrete scoring for reliable critics 23:05 – Tip #3: Snapshot tests to validate migrations 25:45 – Scaling to enterprise codebases 27:49 – Large Codebase SDK & dependency-aware parallelism 29:12 – Key takeaways for AI-driven modernization 30:36 – OpenHands programs, resources & next steps Q&A Session 32:33 – Live Q&A: COBOL migration with AI agents 33:00 – How the critic agent evaluates correctness & quality 35:45 – Reliability with limited COBOL training data 38:00 – Mainframe realities: JCL, DB2, RPG, and legacy systems 40:00 – Long-running agents, context limits & Opus performance 42:00 – Database optimization after modernization 44:30 – Preserving business logic across large codebases 47:00 – Models used, cost considerations & scalability 51:10 – Parallel agents without breaking interfaces 54:55 – Best practices for context engineering at scale 56:25 – Wrap-up & community call invite