LFX Mentorship
Open Mainframe Project - Drive Mainframe Modernization with Agentic AI
Linux Foundation1 Program Years
Open Mainframe Project - Drive Mainframe Modernization with Agentic AI
About the Organization
The program aims to leverage agents built using publicly available large language models (LLMs) and personalize them with Retrieval-Augmented Generation (RAG). This approach creates various levels of agentic roles to transform mainframe workloads and modernize them at every phase, from discovery and design to transformation, testing, and deployment. Problems to be addressed in the project: - Unavailability of application documentation. - Lack of comprehensive view of all tasks to be included in the transformation plan. - Multiple iterations of Code conversions. - Technical and resources dependencies. - Unavailability of Application support for the transformation. Deliverables: - Working Agents which will build a program plan at each of the modernization phase for all workstreams. - Agents performing Code transformation with minimal effort from migration team. - Agents performing application analysis with less dependency from application teams.
Primary Technologies & Languages
data migrationartificial intelligenceailarge language modelsllmragretrieval-augmented generationlarge-language modelsjavacobolpython
Technical Topics & Focus Areas
data migrationartificial intelligenceailarge language modelsllmragretrieval-augmented generationlarge-language modelsjavacobolpython
Participation Years
Completed Projects Distribution
Annual Completed Projects History
Open Mainframe Project - Drive Mainframe Modernization with Agentic AIHover over bars to view completed counts
4
3
2
1
0
2025
Projects1
Years Active1
Stackdata migration
TierTier 1
Past Projects
Showing 1 of 1 projects. Click any project card for scope, mentors, and proposal studio.
Open Mainframe Project - Drive Mainframe Modernization with Agentic AI Mentorship Project
IntermediateThe program aims to leverage agents built using publicly available large language models (LLMs) and personalize them with Retrieval-Augmented Generation (RAG). This approach creates various levels of agentic roles to transform mainframe workloads and modernize them at every phase, from discovery and design to transformation, testing, and deployment. Problems to be addressed in the project: - Unavailability of application documentation. - Lack of comprehensive view of all tasks to be included in the transformation plan. - Multiple iterations of Code conversions. - Technical and resources dependencies. - Unavailability of Application support for the transformation. Deliverables: - Working Agents which will build a program plan at each of the modernization phase for all workstreams. - Agents performing Code transformation with minimal effort from migration team. - Agents performing application analysis with less dependency from application teams.