The Private AI Platform: Why Agentic Apps Need a Private Application Platform

As AI moves from chat demos to “do-things” software, the platform requirements change: identity, policy, network controls, data governance, audit, and safe tool execution become mandatory. This session explains why a private application platform on private cloud is the most reliable foundation for enterprise AI—especially for agentic workflows and development assistants that touch sensitive systems. We’ll map the core platform capabilities (runtime, workload identity, secrets, supply chain, service brokering, observability, guardrails) to real AI components: model endpoints, vector stores, tool APIs, MCP servers, and skills routing. You’ll leave with a reference architecture and a pragmatic checklist to evaluate whether your current platform can safely run “full-blown” AI experiences.