AI Agents for Gemini: Options, Capabilities, and Setup

Yes, Gemini offers agentic AI — but the form it takes depends on whether you are a personal user, a Google Workspace subscriber, or an enterprise customer. Personal accounts can use Deep Research in Gemini Advanced and customized Gems (becoming Skills in November 2026). Enterprise teams have Google Agentspace, which connects agents to company data sources. Tools like fenbs are useful alongside any of these when you need a shared record of project decisions and handoff notes that the agents can read at the start of a session.

This guide maps the agent options across Gemini products, distinguishes chat from agentic actions, and explains what each one needs to work well.

Does Gemini offer agentic AI?

Gemini offers agentic features, though not all Gemini products have them equally. "Agentic" here means the AI takes a sequence of actions — browsing, reading sources, writing — rather than just answering a single question. The clearest example in personal Gemini is Deep Research, which takes a query and autonomously browses the web to produce a structured research report. That is distinct from asking Gemini a question and reading its answer.

In Google Workspace, Gemini can take actions within apps — drafting and sending emails, creating Docs, updating Sheets — when given permission. In enterprise, Google Agentspace can run multi-step workflows across connected data sources. The common thread is a goal supplied by the person, a sequence of steps the agent decides, and an output to review.

Personal Gemini agentic options

Personal users with Gemini Advanced access have two main agentic features: Deep Research and Gems (Skills).

• Deep Research: give Gemini a research question and it autonomously browses multiple sources, synthesizes findings, and produces a structured report with citations. You review and approve the plan before it starts.
• Gems (becoming Skills from November 2026): customized Gemini experts you configure with a name, instructions, and knowledge. They are specialized conversation partners rather than autonomous agents, but they carry a persistent brief across sessions.
• Gemini Notebook (renamed from NotebookLM in July 2026): upload sources and ask questions against them; not an agent in the autonomous sense, but deeply agentic in how it synthesizes across many documents.

For specifics on what each feature includes in your plan, check the Gemini product pages — plan tiers and feature availability change. The rename of Gems to Skills is confirmed to start in November 2026; verify the current name before relying on it in instructions.

Enterprise Gemini agent options

Enterprise teams using Google Workspace have a broader set of agentic tools.

• Gemini in Workspace apps: actions inside Docs, Sheets, Gmail, Meet, and Drive — summaries, drafts, structured data extraction — with user approval at each step.
• Google Agentspace: an enterprise platform where agents connect to internal data sources (Google Drive, SharePoint, Salesforce, ServiceNow, and others) and run multi-step research and workflow tasks. Agents can be built with pre-built templates or configured for specific workflows.
• Vertex AI Agent Builder: a developer platform for building custom agents on Google infrastructure, with connections to company data and external APIs.
• NotebookLM / Gemini Notebook in Workspace: shared notebooks for team research and synthesis across uploaded sources.

Google Agentspace is the most complete enterprise agent option: it surfaces agents in a single interface where employees can run research, find expertise across the organization, and trigger workflow steps. For current availability and feature details, Google's Agentspace product page is the right source.

How Gemini agents differ from a regular Gemini chat

A regular Gemini chat responds to one prompt at a time. An agentic Gemini feature runs a plan: it decides what steps to take, executes them in sequence, and produces a result at the end. Deep Research is the clearest example — Gemini browses dozens of sources before you see any output. You do not prompt each browse; you review the output when it finishes.

That difference in control matters for how you prepare. A conversational chat needs a clear question. An agentic task needs a clear goal, the boundaries of what it may access or produce, and a review step before the output is used.

What to set up before using Gemini agents

Before running an agentic Gemini task, be clear about three things: the goal (one or two sentences, specific), the scope (what sources it may use, what it should not include), and the output format (a report, a table, a draft, a set of action items). Vague goals produce vague outputs regardless of how capable the agent is.

For recurring tasks — weekly competitive monitoring, periodic project reviews — write a reusable brief. Store it somewhere you can copy from, or keep it in a shared project record that a connected assistant reads at the start of each session.

How to use AI agents for Gemini with fenbs

fenbs and Gemini CLI work together natively; for other Gemini products, fenbs provides the context you paste in.

1. Create a free account at fenbs and add a project for your Gemini-assisted work.
2. Write the standing rules in Decisions and rules: what sources to use, what to avoid, the expected output format.
3. For Gemini CLI: connect from Integrations so the session reads project context automatically.
4. For Gemini Advanced or Agentspace: copy the current tasks and rules from fenbs and paste them as the starting brief.
5. After the session, record what was completed and what needs review in fenbs so the next session starts from the confirmed state.

For an overview of how different agent types compare, types of AI agents explains the main categories. For patterns that apply across tools, AI agents for beginners is a good starting point.

When Gemini agents are not the right fit

Gemini agents work well for research synthesis, document-based analysis, and workflow steps inside Google Workspace. They are less suited to tasks that require connecting to non-Google systems (outside Agentspace's connectors), producing code for a non-Google codebase, or running in environments where you need fine-grained control over each step. For those cases, a developer-oriented agent tool or a coding assistant with MCP connections will be a better fit.