This topic covers the infrastructure and server stacks needed to deploy and scale agentic AI systems: hybrid compute and storage architectures, orchestration layers, data-plane integrations, and deployment tooling. Vendor offerings such as Dell AI Factory and NVIDIA’s Vera family (including Vera CPUs) exemplify the move toward integrated server platforms and specialized silicon that support high-throughput inference, model serving, and tighter hardware/software co-design. Relevance (June 11, 2026): agentic AI workloads increasingly demand low-latency model execution, large memory and networking footprints, and predictable cost/observability across cloud and on-prem environments. That trend drives adoption of Kubernetes-centric orchestration, hypervisor and MCP deployment tools, and richer cloud data connectors so agents can act on live data stores and messaging systems while meeting governance and cost constraints. Key components and tools: Kubernetes (MCP Server Kubernetes) provides unified cluster management and pod/service lifecycle control; Dagster offers pipeline orchestration for data and model workflows; Confluent/Kafka MCP servers enable real-time messaging access; Snowflake and BigQuery MCP servers expose cloud data platforms to LLMs for secure querying; AWS and AWS Cost Explorer MCP servers surface resource operations and spend/Bedrock usage for governance and cost optimization. These MCP (Model Context Protocol) integrations let agentic systems interact with cloud APIs and data stores in a structured, auditable way. Practical takeaway: scalable agentic AI stacks combine specialized hardware, container/Kubernetes orchestration, data pipeline orchestration, and MCP-enabled connectors for secure access to data and cloud services. Architecture choices should prioritize data locality, observability, cost control, and interoperability between on-prem servers and cloud platforms to support production-grade autonomous agents.