IT leaders are facing pressure to build and deploy AI agents quickly, but the underlying engineering complexity is significant. This complexity involves fragmented tools, data security concerns, and budget management. The Gemini Enterprise Agent Platform aims to simplify this by providing a unified environment for building, scaling, governing, and optimizing agents. To navigate these challenges, it's crucial to ask engineering teams specific questions.The build phase begins by understanding who is building the applications, as AI creation is no longer exclusive to high-code engineers. Developers need specialized AI tools to accelerate coding, but these often lack connection to essential enterprise data. Google Antigravity with specific extensions is recommended for core application, data, and Google Cloud engineers.It's essential to determine if agents are being built for human interaction or for other agents, as this dictates design requirements. For human interaction, focus on user experience; for agent-to-agent communication, prioritize interoperability using protocols like Agent2Agent. Choosing the right development tool involves considering a four-rung ladder: Agent Studio for low-code, Managed Agents API for agent-as-a-service, Antigravity 2.0 for advanced coding, and Agent Development Kit (ADK 2.0) for highly custom networks.For initial development, starting with a single, specialized agent is advised to maintain accuracy and efficiency. As complexity grows, transitioning to a multi-agent system where specialized agents collaborate is recommended. Connecting enterprise data requires open standards like Model Context Protocol (MCP) to provide agents with necessary context and logic for accurate decision-making.To ensure agents built on different frameworks can communicate, the Agent2Agent (A2A) protocol enables universal communication. Agents should dynamically retrieve needed tools using focused agentic Skills to avoid performance degradation and cost increases. Scaling requires deploying agents in a fully managed, serverless execution environment like Agent Runtime, offering elastic auto-scaling and secure private networking.To manage long-running tasks, agents need both short-term and long-term memory, with Agent Platform handling immediate session state and persistent storage. Limiting the blast radius for agents running scripts or browsing the web is crucial; this is achieved by executing such tasks in temporary, isolated sandbox environments.