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C# & AI Masterclass: Cloud-Native AI & Microservices. Containerizing Agents and Scaling Inference.
Stop scripting and start engineering the next generation of cloud-native AI. Master C# and Kubernetes to build resilient, distributed agent swarms at scale. Bridge the gap between experimental logic and high-performance, production-grade systems. Architect the future of intelligence—from event-driven scaling to secure service meshes.
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About
About the Book
Architect the Future of Intelligent Systems with C# and Kubernetes
The era of the monolithic AI script is over. To build the next generation of enterprise software, you must stop treating AI agents as experimental toys and start treating them as resilient, scalable cloud-native microservices.
Cloud-Native AI Agents with C# is the definitive guide for .NET architects and developers who need to operationalize Artificial Intelligence at scale. This advanced volume moves beyond the basics of API calls, taking you deep into the engineering challenges of orchestrating distributed agent swarms.
In this book, you will not just write code; you will design systems. You will learn how to decouple cognitive logic from execution environments, manage expensive GPU resources efficiently, and build self-healing infrastructure that adapts to bursty inference workloads.
What You Will Build and Master:
- Containerization Strategies: Master multi-stage builds to package Python/C# hybrid agents into secure, lightweight containers.
- Advanced Orchestration: Use Kubernetes Operators and StatefulSets to manage the complex lifecycle of stateful agents.
- Event-Driven Scaling (KEDA): Implement autoscaling architectures that react to RabbitMQ queue depth and Kafka lag, ensuring your agents scale to zero and burst to infinity.
- High-Performance Inference: Implement dynamic batching, GPU partitioning, and asynchronous pipelines to maximize hardware ROI.
- Service Mesh Integration: Secure your agent swarm with Istio, enabling mTLS, circuit breaking, and canary deployments for new model versions.
- Capstone Project: Combine every concept to build a production-grade Distributed RAG (Retrieval-Augmented Generation) Pipeline from scratch.
Whether you are building a customer support bot that handles thousands of concurrent users or a complex multi-agent system for automated reasoning, this book provides the architectural patterns, C# code samples, and Kubernetes manifests you need to succeed.
Stop scripting. Start engineering. Scale your intelligence.
Check also the other books in this series
Table of contents
Chapter 1: Foundations of Cloud-Native AI: From Monoliths to Microservices
Chapter 2: Your First Agent: Containerizing a Simple C# Microservice
Chapter 3: The AI Agent as a Microservice: Core Principles and Design Patterns
Chapter 4: Advanced Containerization: Optimizing Runtimes for AI Workloads
Chapter 5: Scaling Principles: Latency, Throughput, and State Management
Chapter 6: Architecting for Inference: The Role of C# and Modern .NET
Chapter 7: Stateful Intelligence: Managing Agent Lifecycles with Kubernetes Operators
Chapter 8: Building the Control Plane: Agent Orchestration in C#
Chapter 9: The Distributed Nervous System: Inter-Agent Communication Patterns
Chapter 10: Dynamic Scaling: Orchestration with Kubernetes Autoscalers
Chapter 11: Scaling Inference Pipelines: From Theory to Practice
Chapter 12: High-Throughput Inference: Implementing Asynchronous Pipelines
Chapter 13: The Agent's Toolbox: Integrating External Services with MCP and .NET
Chapter 14: High-Performance Patterns: GPU Resource Management and Batching
Chapter 15: Scaling Inference Workloads: From HPA to Event-Driven Autoscaling
Chapter 16: Production-Ready Agents: CI/CD and Zero-Downtime Model Updates
Chapter 17: Infrastructure as Code: Managing Agent Deployments with Kubernetes Operators
Chapter 18: The Service Mesh: Resilience and Observability with Istio
Chapter 19: Persistent Intelligence: Managing Model Weights with Kubernetes Storage
Chapter 20: Orchestrating Agent Swarms: High-Throughput Inference with KEDA
Chapter 21: Advanced Orchestration: Building Custom Controllers and Schedulers
Chapter 22: Operationalizing Agent Lifecycles: Health Checks and Graceful Shutdowns
Chapter 23: Architecting Distributed Inference: Dynamic Batching and Stateful Orchestration
Chapter 24: From Agent to Swarm: Managing State and Communication at Scale
Chapter 25: Advanced Orchestration: GPU Partitioning and Stateful Agent Swarms
Chapter 26: Capstone Project: Building a Distributed Retrieval-Augmented Generation (RAG) Pipeline
If printed, this ebook would span over 300 pages. Each chapter is structured into theoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios with complete solutions.
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Author
About the Author
Edgar Milvus
A veteran software engineer with 20 years of experience, I have dedicated my career to the art of automation. My philosophy is simple: programming should eliminate repetitive chores to unlock human creativity. This journey began early on with the development of custom code-generation tools and has evolved into a deep mastery of LLMs and their APIs. Today, I specialize in architecting AI-driven solutions that handle everything from complex coding and security tasks to advanced knowledge retrieval, transforming the way we interact with technology
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