Agentic AI Workflows shows engineers and automation practitioners how to design, build, and operate production-grade AI systems using LangChain, n8n, and multi-agent orchestration. Unlike theory-heavy surveys, this hands-on guide focuses on practical patterns: chaining prompts and tools, building retrieval-augmented pipelines, managing memory and context, and implementing observability, governance, and safe human-in-the-loop checks.
You'll get end-to-end examples - from LangChain chains and LCEL templates to n8n evented flows and multi-agent coordinator patterns - plus real-world strategies for cost control, scaling on Kubernetes, and recovering from failures. Each chapter includes clear checklists, reproducible architectures, and playbooks you can use immediately in enterprise and startup environments.
If you lead automation, platform engineering, or AI product teams, this book gives you the workflow-first playbook to take LLM experiments into reliable production: leaner builds, safer automation, and agents that actually help people make better decisions.