Platform Native Agentic AI vs. Public LLMs

Why architecture matters for MSPs

Many MSPs are already experimenting with AI tools such as ChatGPT or Gemini. But using a public LLM is fundamentally different from deploying platform-native agentic AI inside your PSA and RMM.

In this session, we’ll explore why architecture matters and what separates conversational AI tools from AI systems designed to operate inside real service workflows.

This webinar continues our five-part educational series designed to clarify what agentic AI is, how it differs from public LLM tools, and why those differences matter for MSPs and IT teams.

We will unpack:

• Why conversational models alone cannot operate your business
• The limitations of standalone LLM tools and AI overlays
• How platform-native agentic AI uses infrastructure, context, and scaffolding to drive action
• How guardrails, business logic, and learning systems improve accuracy over time
• Why security, compliance, and hallucination control are critical in IT environments
• How ConnectWise approaches data isolation, encryption, and governance 

Even if you already use AI, this session will help clarify the difference between experimenting with AI tools and deploying AI systems built to operate inside your MSP.

You’ll walk away with a clearer understanding of why infrastructure and platform integration are required for AI to move beyond conversation and into execution.