# Gemini across your tools and the ecosystem
The Gemini app is the front door, but most of the value lives in the rooms behind it: Chrome, Android, Workspace, the APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète →, and a growing layer of third-party tools built on Google's models. Knowing which surface to reachreachThe number of unique people exposed to your message in a given period. Unlike impressions, reach counts each person once, no matter how often they see it.Voir la définition complète → for is the difference between a clever demo and something that actually saves you hours every week.
This lesson maps those surfaces and, more importantly, tells you when each one is the right tool. We'll end with a concrete setup so you can see the decision in action.
Every surface here runs the same underlying Gemini models (the Pro tier for harder reasoning, the Flash tier for fast, cheap, high-volume work). What changes is the *body* around the brain: what data it can see, what it can act on, and who controls it.
Three questions decide everything:
1. Where does the context live? Your inbox? A codebase? A database you own?
2. Who triggers it? You, manually? A schedule? Another system?
3. Who needs to control or audit it? Just you? Your whole org? A compliance team?
Hold those three questions. They mapmapUsing software to automate repetitive marketing tasks and campaigns, enabling personalisation at scale across channels like email, web, and social.Voir la définition complète → cleanly onto the surfaces.
The app is for open-ended, interactive work. Gems (your saved, reusable custom assistants with fixed instructions) are the right call when you repeat the same *kind* of task with different inputs: a "release notes drafter" Gem, a "rewrite this in our brand voice" Gem. Build the instruction once, reuse it daily.
Gemini is woven into the OS and the browser, so the context is *whatever is on your screen*. On Android, Gemini can read the current app's context to answer "what's this email asking me to do?" without copy-paste. In Chrome, it can summarize or reason about the page you're on.
Use these when the data is ephemeral and in front of you. Don't use them for anything you need to repeat or audit, because there's no artifact to save.
This is the highest-leverage consumer surface for most professionals, because the context is *your own documents and mail*, governed by your existing permissions. Gemini in Docs, Gmail, Sheets, Slides, Meet, and Drive sees only what you already have access to.
Concrete uses:
The rule: if the data is already in Workspace and a human is in the loop, use Workspace Gemini. You get grounding in your real content for free. Details and current capabilities live at support.google.com under the Workspace help center.
Workspace Gemini is interactive. The moment you want something to run *on a schedule* or *in response to an event* (a new form submission, a row added to a sheet), you cross into automation, and the right tool is Apps Script: Google's built-in JavaScript runtime for Workspace.
Apps Script can call Gemini and write the result straight back into a Doc, Sheet, or email. Here's a trigger-driven snippet that summarizes a new support ticket row using the Gemini APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète → and writes the summary to the next column:
function summarizeNewTicket(e) {
const apiKey = PropertiesService.getScriptProperties().getProperty('GEMINI_KEY');
const ticket = e.values[1];
const url = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=${apiKey}`;
const payload = {
contents: [{ parts: [{ text: `Summarize this ticket in one sentence: ${ticket}` }] }]
};
const res = UrlFetchApp.fetch(url, {
method: 'post',
contentType: 'application/json',
payload: JSON.stringify(payload)
});
const summary = JSON.parse(res.getContentValue ? res.getContentText() : res.getContentText())
.candidates[0].content.parts[0].text;
e.range.getSheet().getRange(e.range.getRow(), 3).setValue(summary);
}This is the bridge surface: no servers, no deployment, and it lives inside Google's permission model. ReachReachThe number of unique people exposed to your message in a given period. Unlike impressions, reach counts each person once, no matter how often they see it.Voir la définition complète → for Apps Script when the trigger is a Workspace event and the output goes back into Workspace.
When you outgrow Workspace (custom apps, your own dataown dataData collected directly from your own customers and prospects through your own channels: your most reliable and privacy-compliant source.Voir la définition complète →, your own users), you move to the developer side.
Google AI Studio is the fastest place to prototype. You write a prompt, tune it, and export working code in one screen. The same models you tested are available through the Gemini API, documented at ai.google.dev. This is where you exploit the features the consumer app hides from you: explicit model selection, native multimodality (passing images, audio, video, and PDFs directly), long context for whole-document reasoning, and grounding with Google Search as a toggle.
Grounding is the one to internalize. Instead of building a full RAG pipelinepipelineAll active sales opportunities across the stages of the sales process, together with their combined potential value and probability of closing.Voir la définition complète → for general world facts, you enable Search grounding and Gemini fetches and cites live results itself:
from google import genai
from google.genai import types
client = genai.Client(api_key="YOUR_KEY")
response = client.models.generate_content(
model="gemini-2.5-flash",
contents="What changed in the latest EU AI Act guidance this month?",
config=types.GenerateContentConfig(
tools=[types.Tool(google_search=types.GoogleSearch())]
)
)
print(response.text)Use the APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète → when you're building a product, embeddingembeddingAn embedding is a numerical vector that represents data (text, images, or items) in a way that captures meaning, so similar items sit close together in space.Voir la définition complète → AI in your own app, or serving users who aren't you. Use AI Studio first to get the prompt and config right.
Two developer surfaces deserve their own mention because they live where engineers actually work.
Gemini CLI brings the model into your terminal: ask it to explain a stack trace, refactor a file, or run a multi-step task against your local project. Gemini Code Assist lives in your IDE (VS Code, JetBrains) and in GitHub, doing completion, review, and chat with awareness of your codebase.
The distinction: CLI is for ad-hoc, conversational, terminal-native work and quick agentic tasks on local files. Code Assist is for the continuous, in-editor flow. Both are the right surface when the *context is your code*.
🎬 [VIDEO: "Gemini CLI: Your open-source AI agent in the terminal" - youtube.com/watch?v= tutorial - a walkthrough of installing the CLI and running agentic tasks against a local repo]
Vérification des acquis
1. According to the lesson, what distinguishes the various Gemini surfaces (Chrome, Android, Workspace, API) from one another?
2. When are Gems described as the right tool to reach for?
3. In the lesson's framing, which Gemini tier is positioned for fast, cheap, high-volume work?
4. Select ALL correct answers about the three questions the lesson says decide which surface to use.
Sélectionnez toutes les réponses correctes.
5. Select ALL correct answers about how Gemini behaves in Chrome and Android per the lesson.
Sélectionnez toutes les réponses correctes.
Two more surfaces matter once you go beyond a single APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète → call.
When your task needs *multiple steps, tools, and decisions* (call an APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète →, check a result, decide what to do next), you're building an agent, and a raw generateContent loop gets messy fast. The Agent Development Kit is Google's open-source framework for building, structuring, and orchestrating agents with tools, memory, and multi-agent handoffs in a clean, testable way.
A minimal ADK agent that can use a tool looks like this:
from google.adk.agents import Agent
def get_order_status(order_id: str) -> dict:
"""Look up the status of a customer order by its ID."""
return {"order_id": order_id, "status": "shipped", "eta": "2 days"}
root_agent = Agent(
name="support_agent",
model="gemini-2.5-flash",
instruction="Help customers check order status. Use the tool for any order ID.",
tools=[get_order_status]
)ReachReachThe number of unique people exposed to your message in a given period. Unlike impressions, reach counts each person once, no matter how often they see it.Voir la définition complète → for ADK when the logic is genuinely agentic: branching, tool use, and steps that depend on prior results. For a single prompt-and-response, ADK is overkill; stick with the APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète →.
Vertex AI is the enterprise surface on Google Cloud. The models are the same, but the body around them is built for production: IAM permissions, VPC controls, regional data residency, monitoring, scaling, and integration with your cloud data warehousedata warehouseA central repository that consolidates data from many source systems into a structured, query-optimized store designed for analytics, reporting, and business intelligence.Voir la définition complète →. ADK agents deploy here, to a managed runtime, when they need to run reliably for real users.
The decision between the Gemini APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète → and Vertex AI is about *who controls and audits it*. Solo project or startup moving fast: the Gemini APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète → on an APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète → key. Enterprise with security, compliance, and data-governance requirements:
You'll also meet Gemini *inside other tools*: products that wired Google's models into their own workflows through the APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète →. The point isn't to list them, it's to recognize the pattern. When you evaluate one, ask the same three questions: where's the context, who triggers it, who audits it. A tool that sends your data to Gemini through someone else's account changes the answer to all three.
Say you run customer support and want incoming tickets summarized and routed.
Same job. Five surfaces. The right one is the smallest surface that satisfies your three questions, and not one step heavier.