Publisher's Synopsis
As AI systems become more autonomous, dynamic, and deeply embedded in critical infrastructure, a silent threat grows louder-technical debt. But in agentic AI systems, this debt isn't just code rot or bad architecture-it's biased behavior, ethical drift, model decay, and automation that breaks before it bends.
This book is your no-fluff, high-depth guide to understanding and managing technical debt in the AI era.
Written for AI engineers, product leaders, system architects, and curious technologists, Technical Debt in Agentic AI Systems unpacks:
The unique nature of technical debt in agentic and autonomous systems
Real-world breakdowns across healthcare, finance, manufacturing, and gaming
The architecture, data, integration, and automation-driven sources of debt
AI-powered frameworks for debt measurement, monitoring, and refactoring
Practical mitigation strategies that align business impact with engineering trade-offs
Future trends-from autonomous refactoring to debt-aware generative AI prompts
Supported by rich case studies, industry benchmarks, and actionable frameworks, this book offers a structured playbook for staying ahead of the AI curve-without drowning in legacy systems and cascading failures.
Whether you're building the next-gen AI platform or managing an enterprise automation portfolio, this refresher will help you shift from reactive firefighting to strategic debt management.
Get ready to turn technical debt from a liability into a competitive advantage.
