Short answer: Google just tied together a workplace-ready agent platform (Gemini Enterprise), a developer-first command-line agent (Gemini CLI), and a monster inference chip (Ironwood TPU). Together, they promise faster builds, lower latency, and safer, governable AI agents that actually talk to your data and apps.
TL;DR (for busy builders)
- Gemini Enterprise is the “front door for AI at work” — a secure, searchable, agentic layer that connects to Google and third-party apps (SharePoint, Jira, ServiceNow, Salesforce, Slack, etc.), ships with governance, and includes no-code agent tools and NotebookLM Enterprise. Editions (Standard/Plus/Frontline) vary by storage, features, and governance depth. Pricing reported: from $30 per seat/mo for Enterprise editions; Gemini Business at $21 per seat/mo with a 30-day trial for smaller teams.
- Gemini CLI is an open-source terminal agent (ReAct loop, MCP support, search grounding, file & shell tools). Install via npm/Homebrew and use it free with a personal Google account quotas (e.g., 60 req/min and 1,000 req/day), or behind enterprise quotas via Google Cloud.
- Ironwood TPU (Google’s 7th-gen TPU for inference) scales to 9,216 chips per pod with up to ~42.5 exaFLOPS, 192 GB HBM per chip, ~7.3 TB/s bandwidth, and 2× power efficiency vs Trillium — the silicon backbone behind Gemini’s speed and context capacity.
Part 1 — Gemini Enterprise: the workplace “front door” for AI
What it is
Gemini Enterprise unifies enterprise search, chat, agentic workflows and data connectors so every employee can discover, create, share, and run AI agents in one secure environment. Think: ask a question → get a grounded answer → take action in connected apps — all governed, logged, and permissions-aware.
Core building blocks (at a glance)
- Data connectors & federation: Confluence, Jira, Microsoft SharePoint/OneDrive/Outlook/Teams, ServiceNow, Salesforce, Slack, Box, Google Drive, BigQuery, Cloud SQL/Spanner/Firestore, and more. Self-hosted sources connect privately via Private Service Connect. (Google Cloud)
- Apps, Assistants, Actions & Agents: No-code Agent Designer to publish guided automations; permission-aware actions across Gmail/Calendar, Jira, Outlook, ServiceNow; full-code agents welcome too.
- Security & governance: IAM roles/permissions, audit logging, access transparency, VPC-SC, customer-managed encryption keys; enterprise analytics to monitor usage and long-running ops.
- Protocols & ecosystem: Interop across agents via Agent2Agent (A2A); agent-led commerce via Agent Payments Protocol (AP2).
Editions & what they include
Google documents Standard, Plus, and Frontline editions. All include enterprise-grade security, search over connected data, and access to the connector ecosystem. Plus adds larger pooled storage, priority model access, out-of-box agents (like Deep Research), and advanced governance; Frontline is for high-scale frontline users consuming centrally built agents.
Pricing signal: Google and top tech media report Enterprise (Standard/Plus) starting at $30/seat/mo and a Gemini Business tier at $21/seat/mo (annual), with a 30-day trial — ideal for small orgs or departmental pilots. Always confirm region/contract terms with Google sales.
Why teams care
- Reduce swivel-chair work: Ask in one place; the agent searches across apps, cites sources, then acts. (Google Cloud)
- Meet users where they are: Works alongside Microsoft 365 and Salesforce — not just Google apps. (Google Cloud)
- Governance from day one: Admin controls, logging, and permission-aware retrieval ease InfoSec reviews. (Google Cloud)
Part 2 — Gemini CLI: a terminal-first agent for builders
What it is
An open-source AI agent that brings Gemini directly into your terminal. It runs a ReAct loop, can call built-in tools (search grounding, file ops, web fetch), and integrates MCP servers for custom capabilities. It also powers Gemini Code Assist agent mode in VS Code and is available out-of-the-box in Cloud Shell.
Why developers love it
- Fast local iterations with 1M-token context (Gemini 2.5 Pro) and checkpointable conversations.
- Extensible via Gemini CLI Extensions (from Google and partners such as Dynatrace, Elastic, Shopify, Stripe, Snyk, Postman, Figma, etc.). Install with a single command and the “playbook” teaches the agent the tool instantly. (blog.google)
- Generous personal quotas: the repo advertises free usage with a personal Google account (e.g., 60 req/min & 1,000 req/day); enterprises can route via org quotas. (GitHub)
3-minute setup
# Run instantly (no install)
npx https://github.com/google-gemini/gemini-cli
# or install globally
npm install -g @google/gemini-cli
# macOS/Linux users can also:
brew install gemini-cli
(Requires Node.js 20+; works on macOS/Linux/Windows.)
Pro tip: Start in Cloud Shell to skip local auth and environment setup; CLI is pre-enabled there.
Part 3 — Ironwood TPU: the inference engine under the hood
Google’s Ironwood TPU is purpose-built for massive-scale inference (LLMs, MoE, reasoning). That matters for anyone chasing lower latency, longer contexts, and better cost/performance when running production agents.
Key specs Google has published:
- Up to 9,216 chips per pod delivering ~42.5 exaFLOPS.
- 192 GB HBM per chip (≈6× Trillium) and ≈7.3 TB/s HBM bandwidth (≈4.5× Trillium).
- ~2× power efficiency vs Trillium.
Why it matters to you: You don’t rent Ironwood chips directly; you feel them. When Gemini Enterprise or Vertex AI serve your agents/models, Ironwood’s capacity/bandwidth translates to snappier responses, bigger inputs/outputs, and better total cost of ownership at scale.
How the pieces fit together (a practical workflow)
- Prototype in the terminal with Gemini CLI
- Ground with Google Search, browse docs, write tests, and script tasks. Add an extension (e.g., Stripe or Shopify) to simulate an end-to-end workflow.
- Publish a no-code agent in Gemini Enterprise
- Use Agent Designer to encapsulate your prompts, goals, and actions; publish to your org with guardrails and logging.
- Connect enterprise data
- Federate data from SharePoint/Confluence/ServiceNow/Salesforce; keep permissions intact; or connect self-hosted sources via Private Service Connect.
- Govern and scale
- Apply IAM, VPC-SC, CMEK; monitor long-running tasks and analytics. For cross-tool automation or commerce, wire agents through A2A and AP2.
- Rely on Ironwood at runtime
- As usage surges, Ironwood’s inference throughput keeps latency low and context high—without you touching the silicon.
Gemini Enterprise vs. Vertex AI vs. Microsoft/AWS: positioning notes
- Gemini Enterprise is the employee-facing agent and search/action layer;
Vertex AI remains a separate developer platform for building and hosting models/apps. Use both together when you need managed ML pipelines, fine-tuning, or custom endpoints behind your enterprise agents. - The Enterprise editions (Standard/Plus) start at $30/seat/mo; Gemini Business costs $21/seat/mo annual — a competitive entry point versus rival workplace AI suites.
Quickstart: from local CLI to published enterprise agent
A. Local agent in 5 commands (demo pattern)
# 1) Launch CLI
gemini
# 2) Set context for your repo
echo "Please act as a senior dev on this repo. Optimize build steps." > GEMINI.md
# 3) Add an extension (example: a public GitHub URL)
gemini extensions install https://github.com/your-org/gemini-ext-shopify
# 4) Ground with web/search for up-to-date docs
/web.search "ServiceNow incident API create example"
/web.fetch "https://developer.servicenow.com/.../api"
# 5) Generate a workflow script
/write "Create a script that triages issues, opens a ServiceNow ticket, and posts status to Slack."
(Extensions are one-line installs; the built-in “playbook” teaches the agent how to use the tool.)
B. Publish a no-code agent in Gemini Enterprise
- Agents → Create Agent → name it, set goals/instructions → test in the preview pane → Save & Publish.
- Add actions (Gmail/Calendar/Jira/Outlook/ServiceNow) and connect data stores to ground responses.
FAQ
Is Gemini Enterprise only for Google shops?
No. It ships connectors for Microsoft 365 (SharePoint/OneDrive/Outlook/Teams), Salesforce, ServiceNow, Box, Jira/Confluence, Slack, and more — with permissions-aware search.
Do I need to adopt Vertex AI to use Gemini Enterprise?
Not necessarily. Vertex AI stays separate; use it when you need custom model endpoints, training/fine-tuning, evals, or pipelines.
What does pricing look like?
Media and partner coverage state Enterprise editions start at $30/seat/mo; Gemini Business is $21/seat/mo annual with a 30-day trial. Confirm with Google for your region/contract.
How “real” are agent ecosystems and payments?
Google has published A2A for agent interoperability and AP2 for agent-initiated payments with support from major tech/payment partners (e.g., Salesforce, PayPal, Mastercard, Adyen, Okta, many others).
Do I get access to Ironwood directly?
No; you access its performance indirectly via Google Cloud services that run on it (e.g., Gemini serving). That’s how you see the latency and context benefits.
Bottom line (and a smart adoption path)
- Start small: Pilot Gemini Business ($21/seat/mo) or a narrow Enterprise Standard cohort to prove value in a single workflow (e.g., support triage or sales Q&A).
- Build like a dev, deploy like IT: Prototype with Gemini CLI, then productionize in Gemini Enterprise with governance and auditability.
- Scale with confidence: Let Ironwood TPU handle inference-scale under the hood while your agents expand to more teams and data sources.
If you want, tell me your top two workflows (e.g., “L1 support” or “deal desk”), and I’ll map a concrete 30-day rollout using Gemini CLI → Gemini Enterprise with the least lift and the biggest early win.
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