How AI, RAG, and Agents Transform Mainframe Operations

2026-04-18 · Source: IBM Technology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Robotics & Autonomous Systems · Depth: Intermediate, medium

Summary

AI is increasingly integrated into daily life for productivity, from vacation planning to presentation drafting. Mainframes are mission-critical for everyday transactions, yet face operational challenges including skill shortages, integration with hybrid cloud environments, and accelerating new professional training. General-purpose AI tools, such as large language models (LLMs), often provide inaccurate or insufficiently specific answers when applied to complex mainframe issues, like diagnosing CICS error messages. To address this, Retrieval-Augmented Generation (RAG) is proposed, which grounds LLMs with relevant, up-to-date mainframe documentation, including best practices and client-specific information. This approach aims to deliver more accurate and contextually appropriate responses. Furthermore, agentic AI can automate mainframe operational tasks, such as opening service desk tickets, performing health checks, or optimizing workloads, by integrating with system resources and hybrid cloud services. Combining RAG with agentic AI allows for both well-grounded information and live updates from system agents, enhancing mainframe operations.

Key takeaway

For MLOps Engineers and IT Professionals managing mainframe operations, relying solely on general-purpose AI tools risks inaccurate diagnoses and inefficient workflows. You should prioritize implementing Retrieval-Augmented Generation (RAG) to ground AI responses in specific mainframe documentation and integrate agentic AI for automating routine tasks. This combined approach will ensure more accurate, trusted results and significantly boost operational efficiency, accelerating the training of new mainframe professionals.

Key insights

RAG and agentic AI enhance mainframe operations by providing accurate, context-specific information and automating tasks.

Principles

• Mainframe operations require specialized AI grounding.
• General AI tools often lack domain-specific accuracy.
• Personalization improves AI utility for specific environments.

Method

Implement Retrieval-Augmented Generation (RAG) by ingesting mainframe documentation into an LLM. Integrate agentic AI to automate tasks and provide live system updates, combining both for comprehensive operational support.

In practice

• Use RAG for accurate mainframe error diagnosis.
• Automate service desk tickets with agentic AI.
• Personalize RAG with client-specific best practices.

Topics

• Mainframe Operations
• Artificial Intelligence
• Retrieval-Augmented Generation
• Agentic AI
• Large Language Models

Best for: MLOps Engineer, Automation Engineer, IT Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by IBM Technology.