What are the Components of an Enterprise Agentic Architecture?

AI agents are reshaping software development, operations, data access, and customer interactions by automating not only individual tasks but also the reasoning and coordination between them, creating new technical, organizational, security, and governance requirements. Desktop coding agents typically operate locally with a developer’s credentials and limited blast radius, while cloud agents are long-lived, multi-tenant distributed services that require managed identities, persistent state, scalable concurrency, and strict data isolation. Central to cloud-agent design is the harness, the runtime layer that manages context, sessions, memory, tool use, retries, guardrails, telemetry, and multi-agent orchestration, though its rapidly evolving components can create maintenance challenges. Organizations can adopt pre-built vendor platforms, such as Google Gemini Enterprise, Claude Managed Agents, Salesforce Agentforce, Microsoft Copilot Studio, and ServiceNow AI Agents, for faster deployment with less customization, or assemble pro-code architectures using frameworks and services such as Google ADK, LangGraph, CrewAI, Agent Engine, and various storage, observability, and identity tools for greater flexibility. The text also argues that governed, live enterprise data access is essential for agent performance and presents CData Connect AI as an MCP-based data layer supporting connections to more than 350 enterprise sources.

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