Post by Polansoft

Bridging the Mainframe Knowledge Gap in the Age of Rapid AI Adoption 📈 The mainframe talent challenge is often framed as a training problem. In practice, it is just as much an environment problem. Most new engineers do not struggle first with business logic. They struggle with cognitive overhead: fragmented tools, manual routines, and undocumented workflows. If we want to close the knowledge gap, we need to reduce that overhead from day one: ▪️ git-based version control as the default, ▪️ CI/CD with familiar tooling (Jenkins, pytest, quality gates), ▪️ reproducible build and validation flows instead of manual JCL execution, ▪️ routine operational steps moved out of ISPF into scripts and pipelines, ▪️ industry-standard IDEs for everyday development. This is not about replacing mainframe strengths. It is about adding a modern abstraction layer so engineers can focus on domain understanding and system behavior, not tool survival. AI coding agents make this even more urgent. Agents amplify whatever environment they are placed in. ➖ If your process is inconsistent, implicit, and tribal, they scale confusion. ➖ If your process is structured, testable, and documented, they scale throughput. That is why internal documentation and maintained engineering guides now have direct ROI: ▪️ faster, safer onboarding for humans, ▪️ more reliable harnesses for agents, ▪️ fewer missed edge cases in long-running work. The emerging best practice is clear: define a robust harness (initializer, progress tracking, feature checklist, repeatable testing), then iterate incrementally with strong verification discipline — exactly the direction highlighted in Anthropic’s write-up on long-running agent harnesses: https://lnkd.in/g24MEWgQ In many ways, this is not new at all — it is the same operational standard strong teams have always used for people. The difference is that AI makes the need explicit. For newcomers especially, these structures reduce ambiguity, prevent critical omissions, and shorten the path from “I can run it” to “I understand the domain”. The parallels are direct: what helps a junior engineer ramp safely also helps an agent execute reliably. The point is simple: in many enterprise teams, code is not the first bottleneck. The first bottleneck is cognitive load. Reduce it, and both humans and AI become meaningfully more effective. Insight from Viacheslav Burtsev #Mainframe #MainframeModernization #LegacySystems #EnterpriseIT #DigitalTransformation #SoftwareEngineering #AICode #GenerativeAI #Innovation #TechLeadership