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Build, Scale, and Secure Your AI Agents on Kubernetes Are you a Kubernetes developer struggling with AI agent development? Do you feel overwhelmed by the new challenges of MLOps Kubernetes, like managing GPU resources and securing inter-agent communication, and ensuring reliable deployments? You’ve seen the potential of AI, but you’re missing the blueprint to turn a simple Python script into a robust, production-ready system.
“Agents vs. Agentic AI” is your definitive guide to bridging the gap between AI theory and the practical world of Kubernetes for AI. This book provides a practical, end-to-end blueprint for building and scaling autonomous AI systems. We’ll demystify the Agentic AI architecture and give you the hands-on skills to design, deploy, and manage a team of agents that are secure, efficient, and reliable.
Inside, you’ll discover how to:
Master Production-Grade AI Systems: Deploy and manage your agents with core Kubernetes concepts like Pods, Deployments, and Services, ensuring your applications are not just smart, but production-ready.
Secure Your Multi-Agent Systems: Implement a “zero-trust” AI security model with mTLS and RBAC to protect against new agentic threats like prompt injection.
Optimize Performance with GPU Orchestration: Leverage GPU orchestration and fine-tune resource requests and limits to ensure your LLMs run efficiently and cost-effectively.
Build an Agent with a Brain and Memory: Architect for autonomy by understanding an agent’s memory, planning, and tools. We’ll show you how to implement AI agent memory and the role of vector database deployment in giving your agents long-term knowledge.
Debug with Confidence: Use a full observability stack with centralized logging, custom metrics, and tracing to understand your agent’s thought process and actions.
Stop building side projects and start creating the future of autonomous software. Get your copy today and become an agentic AI master.
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