Tech • AI • Robotics
Build Agents with Gemini API
8/10TL;DR
Google unveiled new Gemini APIs enabling real-time multimodal agents, unified model interaction, and managed sandboxed environments for building autonomous applications.
KEY POINTS
Gemini 3.1 Flash Live powers real-time multimodal agents
The Gemini 3.1 Flash Live model introduces low-latency, real-time interaction across voice, text, images, and video. It operates through a stateful WebSocket connection, allowing continuous streaming of inputs and outputs. The system supports speech-to-speech interaction in over 90 languages, enabling seamless multilingual conversations and live interruption handling.
End-to-end multimodal capabilities
Gemini’s strength lies in its ability to process and generate multiple formats. It can interpret visual scenes, written text, and audio simultaneously, while producing outputs such as speech, images, and video. Demonstrations included generating music on demand using Lyria 3, highlighting integration between conversational agents and generative media tools.
Function calling and tool integration
The platform enables agents to execute actions via tool use and function calls, including built-in integrations like Google Search grounding and custom APIs. This allows agents to retrieve real-time data or trigger external services, moving beyond static responses into actionable workflows.
Direct client-side access with ephemeral tokens
Developers can connect directly to the Live API from client applications using short-lived ephemeral tokens, reducing backend complexity. This approach supports responsive applications such as UI manipulation, live coding assistance, and interactive debugging with immediate feedback.
Introduction of the unified Interactions API
The new Interactions API standardizes how developers interact with both models and agents. A single API call structure supports tasks ranging from simple queries to complex agent workflows. It includes server-side state management, eliminating the need to manually maintain conversation history.
Step-based data model replaces chat turns
Traditional chat-based formats are replaced with a step-based interaction model, where each action—user input, reasoning, tool call, or result—is tracked independently. This structure better reflects how agents operate and simplifies orchestration of multi-step processes.
Managed agents with remote execution environments
Google introduced managed agents like Antigravity, which run in isolated cloud environments capable of executing code, accessing files, and performing tasks autonomously. Each agent operates within its own sandbox, effectively acting as a dedicated virtual machine for every task or user.
Persistent environments and shared context
These environments are persistent and reusable, allowing agents to maintain state across sessions. Multiple agents can also share the same environment, enabling workflows where one agent conducts research and another builds applications using the generated data.
Secure credential handling via proxy system
A proxy mechanism ensures that sensitive credentials such as API keys are never exposed to the agent, even when making authenticated requests. The system intercepts calls and injects credentials securely, addressing a major concern in autonomous agent deployment.
Automated application generation in a single call
Demonstrations showed agents generating complete outputs—such as a weather dashboard or a browser-based game—from a single request. The agent handled planning, coding, testing, and file generation autonomously, returning usable artifacts within minutes.
Developer tooling and agent creation
New tools including a Gemini CLI and prebuilt “skills” simplify agent creation. Developers can define agents with configuration files, attach tools, and scaffold environments with dependencies like Python libraries. Agents can also self-configure their runtime environments.
CONCLUSION
Google’s latest Gemini updates signal a shift toward fully autonomous, multimodal agents that can reason, act, and build within secure cloud environments, significantly lowering the barrier to creating complex AI-driven applications.
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