Apr 28
Loading video player...
Enterprise AI agents need more than local prototypes and manual API pipelines. This video explains how the Gemini Enterprise Agent Platform connects the Agent Development Kit, Vertex AI Agent Engine, Cloud Run, agent CLI, progressive disclosure, centralized agent registry, MCP servers, IAM governance, and the skill factory pattern into one deployable architecture. You’ll see how multi-agent systems move from code-first development to production runtimes, persistent memory, secure API discovery, least-privilege access, and self-extending skills. The focus is practical: reducing DevOps overhead, preventing shadow endpoints, and building governed enterprise-grade AI infrastructure. TimeStamps: 0:00 Why local AI prototypes fail at enterprise scale 0:47 The enterprise deployment gap in agent infrastructure 0:56 Gemini Enterprise Agent Platform architecture overview 1:30 Agent Development Kit and multi-agent templates 2:07 Human-in-the-loop review for delegated agent workflows 2:33 Cloud Run versus Vertex AI Agent Engine 3:38 Agent CLI for AI-assisted cloud deployment 4:07 Progressive disclosure and context window efficiency 5:32 Agent registry, MCP servers, and API governance 7:02 Skill factory pattern for self-extending enterprise agents 🤖 Multi-agent systems, 🏗️ Gemini Enterprise Agent Platform, ⚙️ Agent Development Kit, ☁️ Cloud Run vs Vertex AI Agent Engine, 🔐 IAM governance, 🧠 progressive disclosure, 🔌 MCP servers, 🛠️ skill factory automation Enterprise AI architecture becomes valuable when deployment, governance, memory, and API access work as one system. By combining agent runtimes, registry-based discovery, secure permissions, and automated skill creation, teams can reduce engineering drag, scale infrastructure faster, and turn AI agents into controlled business leverage. #EnterpriseAI #AIAgents #GoogleCloud