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Leading Mainframe Modernization with Agentic AI
Paperback
Series: Mainframes
Publisher: Isaac Nangan
Published: Dec 20 2025
Pages: 170
Weight: 0.45
Height: 0.36 Width: 5.50 Depth: 8.50
Language: English
Mainframes still power the world's most critical systems-but the way we modernize and operate them is changing.
Agentic AI for Mainframes is a practical, leadership-aware guide for professionals who want to modernize mainframe environments without disrupting the mission-critical systems their organizations depend on. This book goes beyond automation and tooling to explain how agentic AI-AI systems that observe, reason, and act with clearly defined boundaries-can safely and responsibly transform mainframe operations.
Written for enterprise technologists, architects, managers, and modernization leaders, this book bridges the gap between legacy system realities and modern AI capabilities. You'll learn how to plan AI-driven modernization initiatives, align them with business strategy, manage risk and compliance, and drive adoption across teams that operate in highly regulated environments.
Rather than chasing hype, this guide focuses on what actually works: incremental modernization, human-in-the-loop design, measurable outcomes, and governance-first thinking. Real-world case patterns, implementation playbooks, and executive communication frameworks help you turn AI potential into operational value.
Inside, you'll discover how to:
- Apply agentic AI safely within mainframe workflows
- Modernize without full system replacement
- Measure and communicate ROI to leadership
- Build trust, adoption, and accountability across teams
- Prepare your organization-and yourself-for continuous modernization
Whether you are just beginning your modernization journey or leading enterprise-wide initiatives, Agentic AI for Mainframes serves as both a learning companion and a long-term reference-helping you modernize with confidence, credibility, and control.
Modernization isn't about replacing the mainframe. It's about making it smarter.
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