Google

Google’s AI Agents: Gemini, Workspace Integrations, and Search

Omaima Mazhar

Omaima Mazhar

— 4 min read

Why Google’s AI Agents Matter Now

Google is weaving AI agents into the tools people use every day—Search, Workspace, and Android—so tasks can move from “ask” to “action.” At the core is Gemini, Google’s multimodal model that can understand and generate text, images, audio, code, and more. For teams evaluating AI Strategy, this cluster looks at how Google’s agents work, where they show up, and how to put them to work in real workflows. For a broader overview, see our ultimate guide on AI agents.

Gemini: The Brain Behind Google’s Agents

Models and capabilities

Google’s Gemini family spans lightweight to advanced models, designed to run on-device (Gemini Nano), in the cloud (Gemini 1.5 Flash/Pro), and across products. Key capabilities include:

  • Long-context reasoning: Gemini can work across large documents, chat threads, and codebases.
  • Multimodal understanding: It can process text, images, and in some cases audio/video, enabling richer instructions and outputs.
  • Tool use and actions: Through extensions and integrations, Gemini can fetch data, draft content, execute steps, and hand off tasks.

In practice, this means Google’s agents don’t just answer questions; they coordinate tasks with context from Gmail, Drive, Docs, and more—subject to your permissions. For production-grade voice interactions, see Voice AI Agents with ElevenLabs: TTS, Dubbing, and Realtime Conversation.

Practical examples with Gemini

  • Sales brief builder: Drop a customer email thread and a slide deck into a prompt; Gemini drafts a tailored one-pager with next-step recommendations.
  • Policy comprehension: Upload a long PDF and ask specific compliance questions; Gemini cites sections and suggests required actions.
  • Support triage: Summarize a week of support tickets, cluster by theme, and draft macros for frequent issues.

In regulated contexts like Healthcare, these patterns support auditability and compliance.

Workspace Integrations: Agents Where Work Happens

Gmail and Chat

Google has embedded Gemini into Gmail and Chat to reduce busywork:

  • Summarize threads: Collapse long email chains into key decisions and open questions.
  • Draft and rewrite: Turn bullet points into customer-ready replies with a chosen tone.
  • Action extraction: Pull tasks and deadlines from messages and create follow-ups.

Example: “Summarize this RFP thread, list requirements, and create a response outline as a Google Doc.” The agent compiles details from the conversation and starts the document for you.

To reduce repetitive steps across email and chat, we help teams implement Automation. If you're exploring comparable patterns outside Google, see ChatGPT as an AI Agent: Workflows, Plugins, and Real-World Use Cases.

Docs, Sheets, and Slides

  • Docs: Outline long-form content, rewrite sections, and add citations from Drive files you specify.
  • Sheets: Generate tables, formulas, and lightweight data extracts from pasted lists or Drive documents.
  • Slides: Draft presentations from briefs; propose structure, speaker notes, and imagery. For design workflows, see AI in Figma: Design Agents, Automation, and Prototyping Plugins.

Tip: Give Gemini structure. For example, “Create a 10-slide pitch. Slide 1: problem; Slide 2: ICP; Slides 3–5: product; Slides 6–7: proof; Slides 8–9: pricing; Slide 10: CTA.” The clearer the schema, the better the output.

Meet and Calendar

  • Meeting preparation: Summarize relevant Docs, emails, and calendar notes into a briefing.
  • Notes and actions: Capture decisions and action items, then send follow-ups post-call.

Example: “Prep me for tomorrow’s renewal call with Acme. Include contract value, last risk notes, and open support tickets.” The Google agent compiles a concise brief with pointers to source files.

Admin and governance in Google Workspace

  • Data controls: Admins can manage access, data regions, and DLP rules; Workspace content is not used to train Google’s general models without your permission.
  • Auditing: Use logs to review agent actions and comply with internal policies.

Before rollout, define which domains, shared drives, and calendars the agent can see. Principle of least privilege keeps outcomes high-quality and low-risk. Teams that require threat modeling, red-teaming, and model risk controls can leverage our AI Security services.

Search: How Google’s AI Changes Discovery

AI Overviews and planning

Google Search now uses generative AI to assemble snapshots for complex queries. Rather than clicking through multiple pages, users can see a synthesized overview, suggested steps, and follow-up prompts. Typical use cases:

  • Planning: “Plan a 4-day trip to Kyoto with kids and rainy-day options.” Google’s AI can propose an itinerary, costs, and packing tips.
  • Product research: “Best running shoes for flat feet, under $120.” It summarizes features, trade-offs, and surfaces relevant options.
  • How-to reasoning: “Migrate a Postgres database to Cloud SQL with minimal downtime.” It presents steps, guardrails, and tools to use.

For users, this shortens time-to-answer. For publishers and brands, it makes clarity, credibility, and structured data in content even more critical to be referenced by Google’s AI.

SEO implications and a practical checklist

To stay visible as Google’s AI synthesizes results, focus on signals that feed high-quality overviews:

  • First-hand expertise: Demonstrate real experience, data, and outcomes. Use specifics, measurements, and clear limitations.
  • Task structure: Provide step-by-step instructions, checklists, and decision trees that Google can parse.
  • Structured data: Use schema for products, FAQs, how-tos, and reviews to help Search understand your pages.
  • Concise summaries: Add executive summaries at the top of long pages for better snippet capture.
  • Freshness and maintenance: Update content regularly; note date/version to signal recency to Google.

Example: Convert a 2,500-word tutorial into a clear outline with a numbered procedure, prerequisites, code blocks, and a short TL;DR. This aligns with how Google’s AI extracts and presents guidance.

Build Your Own Agent with Google’s Stack

Options to consider

  • Gemini API and AI Studio: Rapidly prototype prompts, system instructions, and grounding on your data.
  • Vertex AI and Agent Builder: Enterprise-grade agents that connect to internal systems, search your proprietary content, and execute actions.
  • Extensions and connectors: Integrate with Google services (Drive, Gmail, Calendar, Maps, YouTube) or third-party apps to let agents take real actions.

For compute planning and cost-performance tradeoffs, see Nvidia for AI Agents: GPUs, CUDA, and Inference Acceleration and CoreWeave for AI Agents: Scalable GPU Cloud for Training and Inference.

Example workflow: Support knowledge agent

  • Ingest existing articles, run de-duplication, and apply metadata in a Google Sheet for topics, intent, and freshness.
  • Ground the agent on this content; add tools for ticket lookup and status updates.
  • Deploy in Google Chat for reps: “Summarize ticket 1245, link relevant fixes, and draft reply.”
  • Log actions for QA and continuously fine-tune prompts based on success metrics.

Getting Started: A Pragmatic Playbook

  • Pick one use case: Choose a measurable workflow in Google Workspace (e.g., RFP responses) and define success (time saved, quality score).
  • Design prompts + guardrails: Provide templates, tone, and acceptance criteria. Require sources for claims.
  • Set permissions: Limit agent access to the minimal Gmail labels, Drive folders, and calendars needed.
  • Pilot and measure: Run with a small team for two weeks; compare baseline vs. assisted performance.
  • Harden and scale: Add auditing, DLP, and training; expand to adjacent teams once ROI is clear.

Google’s AI agents are most effective when they’re grounded in your data, given clear instructions, and equipped with narrow, auditable actions. Start small, measure impact, and expand deliberately.

Read more