Business leaders researching Gemini for their organization often run into a confusing problem before they even get to features or pricing: Google has used the name Gemini Enterprise for more than one product, and most of what shows up in a search is describing the wrong one. Getting the naming straight matters, because the actual capabilities, pricing, and buying process differ significantly depending on which product a business is really looking at. This blog explains the th

1. When "Autonomous Agents" Refuse to Act You have spent weeks building an enterprise custom connector in Microsoft Power Platform. You wrote a clean REST API hosted on Azure App Service, secured it with Microsoft Entra ID (formerly Azure Active Directory), imported the OpenAPI specification into Microsoft Copilot Studio, and added it as an Action (Plugin). You enabled Generative Actions (Dynamic Chaining) in Copilot Studio settings. You excitedly open the Test Canvas, type a

Gemini's large context window, native multimodal support, and built-in grounding tools make it a genuinely strong candidate for RAG — but none of these features replace the retrieval architecture, evaluation, and engineering work that actually determines whether a RAG system performs well in production. This guide breaks down where Gemini excels, where its most-marketed features get oversold, and what still matters regardless of which model you choose.