This topic covers the platforms, protocols and operational patterns used to deploy agentic AI at scale—where large language models and autonomous agents need standardized context, secure execution, and distributed orchestration. As of 2026, practitioners are converging on protocols like the Model Context Protocol (MCP) and on hybrid architectures that span Kubernetes clusters, serverless/edge platforms and decentralized networks to meet low-latency, privacy and resilience requirements. Key components include MCP-enabled servers and tool integrations (Cloudflare, Supabase, Browser MCP, pydantic’s mcp-run-python) that let LLMs call external services, mount real-world tools and exchange context between agents. Secure runtime sandboxes such as Daytona and MCP-integrated Python sandboxes (Deno/Pyodide) are used to run AI-generated code with isolation and fine-grained policy controls. Data and workflow orchestration (Dagster, Kiln) tie model-driven decisions into pipelines for ingestion, transformation and observability. Kubernetes and edge orchestration patterns provide lifecycle management, autoscaling and policy enforcement for agent kernels and MCP servers, while emerging decentralized networks are explored for discovery, trust, and distributed state. The relevance is practical: teams need interoperable protocols, auditable tool calls, and hardened runtimes to move from experiments to production in regulated and latency-sensitive domains. Trends influencing adoption include standardization efforts (e.g., Frontier Alliance–style consortia), increased use of edge-hosted MCP servers for privacy and performance, and a split of responsibilities between cloud-managed services and on-prem/edge sandboxes. Successful deployments balance interoperability (MCP/tool integrations), security (sandboxing and RBAC), and operational observability across Kubernetes/cloud/edge and decentralized layers.