Reusable Agentic Infrastructure: A Component Architecture for Scalable Multi-Agent System Deployment in Enterprise Environments

Abstract

Deploying agentic AI systems at enterprise scale introduces a software engineering challenge distinct from individual agent capability: each new agent deployment currently requires redundant implementation of observability, memory management, tool connectivity, human handoff mechanics, and security controls. This per-agent infrastructure rebuild pattern consumes engineering capacity, introduces inconsistency across the agent ecosystem, and creates compounding audit and maintenance burden as the number of deployed agents grows. This paper proposes a reusable agentic infrastructure architecture that encapsulates these cross-cutting concerns into shared, versioned components that all agents in an organization inherit. The architecture defines six infrastructure components — an agent base class, an MCP tool registry, a RAG pipeline module, a memory manager, an observability module, and a human handoff manager — and specifies the design principles governing their composition. A production deployment study at a healthcare SaaS organization demonstrates that adopting this architecture substantially reduced new agent build cycle time and infrastructure-layer code volume, improved cross-agent test coverage, and eliminated observability gaps across the deployed agent fleet. The framework is implemented in Python using LangGraph and Model Context Protocol, and the architecture is specified at a level of abstraction suitable for adoption across enterprise technology stacks.