Abstract

Enterprise adoption of generative AI is rapidly shifting from isolated prompt-driven applications toward complex agentic systems that integrate retrieval, reasoning, and tool execution. As these systems grow in scale, the lack of a standardized interaction model between agents and external capabilities introduces challenges in reliability, observability, security, and operational governance. This paper presents aplat form architecture centered on the Model Context Protocol(MCP)as a first-class systems abstraction for enterprise-scale agentic generative AI. MCP servers act as strongly isolated, capability-oriented services that expose tools, data access, and actions to agents throughwell-defined contracts.This separation enables controlled tool invocation, bounded execution, and fault isolation across complex multi-agent workflows. We describe the architectural principles, execution lifecycle, and operational characteristics of MCP-based platforms, including agent orchestration, context management, latency governance, and failure containment. The paper draws on production deployment experience and provides guidance for building scalable, cost-aware,and reliable agentic AI systems in enterprise environments. This paper presents aplat form architecture centered on the Model Context Protocol(MCP)as a first-class systems abstraction for enterprise-scale agentic generative AI. MCP servers act as strongly isolated, capability-oriented services that expose tools, data access, and actions to agents throughwell-defined contracts.This separation enables controlled tool invocation, bounded execution, and fault isolation across complex multi-agent workflows. We describe the architectural principles, execution lifecycle, and operational characteristics of MCP-based platforms, including agent orchestration, context management, latency governance, and failure containment. The paper draws on production deployment experience and provides guidance for building scalable, cost-aware,and reliable agentic AI systems in enterprise environments. This paper presents aplat form architecture centered on the Model Context Protocol(MCP)as a first-class systems abstraction for enterprise-scale agentic generative AI. MCP servers act as strongly isolated, capability-oriented services that expose tools, data access, and actions to agents throughwell-defined contracts.This separation enables controlled tool invocation, bounded execution, and fault isolation across complex multi-agent workflows. We describe the architectural principles, execution lifecycle, and operational characteristics of MCP-based platforms, including agent orchestration, context management, latency governance, and failure containment. The paper draws on production deployment experience and provides guidance for building scalable, cost-aware,and reliable agentic AI systems in enterprise environments. We describe the architectural principles, execution lifecycle, and operational characteristics of MCP-based platforms, including agent orchestration, context management, latency governance, and failure containment. The paper draws on production deployment experience and provides guidance for building scalable, cost-aware,and reliable agentic AI systems in enterprise environments. MCP-based platforms, including agent orchestration, context management, latency governance, and failure containment. The paper draws on production deployment experience and provides guidance for building scalable, cost-aware,and reliable agentic AI systems in enterprise environments. MCP-based platforms, including agent orchestration, context management, latency governance, and failure containment. The paper draws on production deployment experience and provides guidance for building scalable, cost-aware,and reliable agentic AI systems in enterprise environments.