Agentic AI Systems in Production Architecting, Orchestrating, and Scaling Autonomous Workflows

Agentic AI Systems in Production: Architecting, Orchestrating, and Scaling Autonomous Workflows
English | May 11, 2026 | ASIN: B0H1DN82J7 | 426 pages | Epub | 507.03 KB
AGENTIC AI IS NO LONGER EXPERIMENTAL - IT IS NOW INFRASTRUCTURE.Most AI books stop at prompts, demos, or toy projects. This one does not.Agentic AI Systems in Production is a deep technical field manual for engineers, architects, and infrastructure teams building autonomous AI systems that must survive real-world production environments. This book goes beyond hype and explores what actually happens when probabilistic AI agents collide with distributed systems, GPU bottlenecks, orchestration failures, latency cascades, memory constraints, and enterprise-scale reliability demands.Inside, you will learn how to architect production-grade autonomous workflows using modern infrastructure patterns including Kubernetes, Kafka, Redis, Celery, vector databases, observability stacks, and multi-agent orchestration frameworks. The book treats AI systems as volatile distributed systems requiring rigorous engineering discipline - not magic.Through detailed architectural breakdowns, production incidents, operational tradeoffs, and implementation examples, this guide equips readers to build scalable, fault-tolerant AI infrastructure capable of handling real-world enterprise workloads.WHAT YOU WILL LEARN:* Production Agent Architecture - Design resilient multi-agent execution systems with deterministic orchestration patterns.* Inference Infrastructure - Deploy and scale GPU inference workloads using modern serving architectures and batching strategies.* Memory & Retrieval Systems - Engineer high-performance RAG pipelines, vector search systems, and context management layers.* Distributed Systems for AI - Handle latency, backpressure, concurrency, retries, partitions, and failure recovery in autonomous environments.* Observability & Reliability - Implement tracing, telemetry, monitoring, and incident-response strategies for AI-native systems.* Kubernetes & Autoscaling - Operate agentic systems under burst traffic and enterprise-scale concurrency demands.* Security & Governance - Harden AI infrastructure with guardrails, RBAC controls, sandboxing, and compliance strategies.WHO THIS BOOK IS FOR:This book is designed for backend engineers, AI architects, platform engineers, DevOps teams, SREs, infrastructure engineers, and advanced developers building serious AI systems in production. It assumes familiarity with Python, APIs, distributed systems, and cloud-native infrastructure.If you are responsible for reliability, scalability, orchestration, or operational resilience in AI systems, this book was written for you.Build autonomous systems that scale beyond prototypes - and survive production.