Emerging best practices for managing Model Context Protocol (MCP) and AI agents in production environments are focusing on controlling complexity, improving security, and centralizing control through enterprise-grade architecture. As MCP transitions from experimental to production use, challenges arise from tool complexity and governance issues, prompting organizations like Microsoft, Anthropic, and Gartner to emphasize efficient context window management, deterministic tool orchestration, and active tool management. Lunar's MCPX platform operationalizes these practices by enforcing context efficiency, introducing composite tools for deterministic orchestration, and managing tool exposure through curated groups. Additionally, Lunar supports tool customization for safe reuse, encourages remote-first MCP consumption with guarded local exceptions, and advocates for gateway-centric governance to centralize authentication, authorization, and observability. As the MCP ecosystem matures, architectural discipline becomes essential, shifting responsibility from agents to infrastructure to ensure reliable and secure MCP deployments.