Google AI Agents Explained: Gemini, Agentspace, and More

Google's approach to AI agents in 2026 is the most architecturally ambitious of any major technology company. Rather than building a single agent product, Google has constructed a layered stack: a foundation model, a set of agent primitives embedded into its existing products, a platform for enterprise custom agent development, and an open protocol for agent interoperability.

Understanding each layer helps developers decide which parts of the Google agent ecosystem are relevant to their work.

Last updated: March 2026

Google's Agent Strategy in 2026

Google came to agentic AI with structural advantages that no other company has in the same combination: one of the most capable foundation models available, the world's most widely used productivity suite in Workspace, a leading cloud infrastructure platform in Google Cloud, and deep integration across its consumer products.

The strategy reflects these advantages. Rather than building a standalone agent product, Google embedded agent capabilities into the surfaces where people already do their work and gave developers the tools to build on top of the same infrastructure.

The result is a product family rather than a single product. Gemini is the reasoning engine underneath everything. Agentspace is the enterprise knowledge and productivity layer. Vertex AI Agent Builder is the developer platform. A2A is the interoperability layer across the entire ecosystem.

Gemini as the Foundation Model

Every Google agent product in 2026 runs on Gemini. The current generation Gemini models offer a very large context window, strong reasoning capability, and multimodal input support covering text, images, code, audio, and structured data.

For developer purposes, the key characteristics are the context window size, which enables agents to hold large amounts of information in a single session, and the native function calling interface, which allows agents to invoke external tools in a well-structured, reliable way.

Gemini's multimodal capability is also meaningful for agent use cases. An agent analyzing a dashboard screenshot alongside a data export, or processing a scanned document alongside its extracted text, can work with content that would require preprocessing and conversion in a text-only system.

The model is available through both the Gemini API directly and through Vertex AI, with different pricing tiers and SLA guarantees depending on the access method.

Google Agentspace: Enterprise Knowledge and Actions

Google Agentspace is an enterprise product designed to give employees an AI agent that has access to their organization's knowledge: internal documents, Workspace files, connected data sources, and approved external resources.

The core capability is grounded retrieval. When an employee asks Agentspace a question, it does not rely solely on the underlying model's training data. It searches the organization's connected knowledge sources, retrieves relevant content, and grounds its answer in that content. This dramatically improves the relevance and accuracy of responses for enterprise-specific questions.

Beyond retrieval, Agentspace can take actions on behalf of users: drafting emails in Gmail, generating reports in Docs, creating spreadsheet analyses, scheduling calendar events, and triggering approved workflows. The combination of grounded knowledge retrieval and action-taking is what distinguishes it from a simple search or chatbot product.

From an IT administration perspective, Agentspace has granular access controls. Administrators define which data sources the agent can access, which users have access to which capabilities, and what actions the agent is permitted to take. This is essential for enterprise deployments where data governance is a hard requirement.

Deep Research in Gemini: The Research Agent

Gemini's Deep Research feature operates as a research agent. You describe a research question or topic, and Deep Research runs a multi-step investigation: searching the web, reading sources, identifying gaps, searching for additional sources to fill those gaps, and synthesizing a structured report with citations.

For developers and researchers, this is practically useful. Research that would take hours of manual searching and reading can be produced in minutes, with source citations that allow you to verify specific claims before acting on them.

Deep Research is not infallible. It can misinterpret source content, miss nuanced debates within a field, or fail to surface the most authoritative sources on a niche topic. It works best as a starting point for research rather than a final, authoritative source.

The pattern of combining web search with iterative follow-up queries is directly applicable to building custom research agents using the Gemini API and web browsing tools. Understanding how Deep Research works conceptually helps inform the design of similar agent systems.

Google Workspace Agents: Embedded Intelligence

The Workspace agent integrations in 2026 have matured significantly compared to the initial Duet AI releases. Gemini is now embedded as an agent across Docs, Sheets, Gmail, Meet, and Drive with more sophisticated capabilities than the original autocomplete and draft generation features.

In Docs, the agent can draft entire documents from a brief, revise existing content based on feedback, analyze the document's structure, and suggest improvements. The revision loop is genuinely interactive: you can ask it to make a section more concise, change the tone for a technical audience, or expand a specific argument with additional evidence.

In Sheets, the agent writes complex formulas, generates charts from described requirements, analyzes datasets for patterns, and creates structured summaries. For non-technical users on engineering teams, this makes data analysis significantly more accessible.

In Gmail, the agent drafts context-aware responses based on the full thread history, summarizes long email chains, and identifies action items. This is the most immediately time-saving capability for most knowledge workers.

The Meet integration adds real-time note generation and post-call summary with action items and follow-up suggestions. For remote engineering teams with frequent synchronous meetings, the time savings on note-taking and follow-up documentation are significant.

Vertex AI Agent Builder: Custom Agents on Google Infrastructure

Vertex AI Agent Builder is Google's developer platform for building and deploying custom AI agents on Google Cloud. It provides managed infrastructure for the full agent lifecycle: model selection, tool integration, testing, deployment, monitoring, and scaling.

The platform supports both no-code and code-based agent construction. The visual builder is useful for prototyping and for non-developer use cases. The code-based SDK gives developers full control for production systems.

Key capabilities include the Data Store connector, which allows agents to retrieve information from connected knowledge sources including BigQuery datasets, Cloud Storage buckets, and websites. The Extensions system allows agents to call external APIs and Google Cloud services as tools. Playbooks allow developers to define complex conditional agent behavior using a document-based configuration format.

For teams deploying agents in regulated industries, Vertex AI provides the data residency, compliance certifications, and security controls that are often required before an agent can interact with sensitive organizational data.

Agent2Agent Protocol: Google's Interoperability Standard

Agent2Agent (A2A) is an open protocol specification released by Google in early 2026. Its purpose is to define a standard way for AI agents to discover each other's capabilities and communicate across agent boundaries.

The protocol works through agent cards: structured metadata documents that describe what a given agent can do, what inputs it accepts, and what outputs it produces. An orchestrating agent can discover these cards, understand the available capabilities, and route subtasks to appropriate specialist agents, regardless of which framework or vendor built them.

The significance is interoperability. Before A2A, building a system where a LangChain agent delegates work to a Vertex AI agent requires custom integration code. A2A aims to make this as straightforward as an HTTP request: a standard format that all conforming agents understand.

Anthropic, Microsoft, and several major software vendors expressed early support for A2A in their agent frameworks. Broad adoption will make cross-vendor agent systems significantly easier to build and maintain.

How Google Agents Compare to Competitors

Microsoft's Copilot agents are the most direct competitor. Both Google and Microsoft have embedded agents deeply into their productivity suites, built platform services for custom agent development, and published interoperability specifications. The key difference is the underlying model: Google uses Gemini while Microsoft uses a combination of OpenAI models.

OpenAI Operator is Google's most direct competitor in browser-based autonomous agent capability. Google's approach emphasizes integration with enterprise Workspace data; OpenAI Operator emphasizes the ability to navigate arbitrary websites.

For developers choosing between platforms, the decision often reduces to which cloud ecosystem you are already in. Teams on Google Cloud with Workspace have a natural path to Google's agent stack. Teams on Azure with Microsoft 365 have a natural path to Microsoft's stack.

Limitations of Google's Current Agent Offerings

Google Agentspace has limited availability outside of US-based enterprise accounts and select qualified organizations. The rollout has been deliberately controlled.

The Workspace agents occasionally produce output that matches the format of a thoughtful response without matching the factual depth. The grounding helps, but it does not eliminate confident-sounding inaccuracies.

Vertex AI Agent Builder's visual builder is better suited for prototyping than production. Complex agent behavior that requires fine-grained control over reasoning flows is harder to express in the visual interface and often requires falling back to the code SDK.

The A2A protocol is very new. Ecosystem tooling and community knowledge around it are still developing. For teams considering multi-vendor agent architectures today, plan for the integration complexity to be higher than the protocol specification alone suggests.

How to Start Building with Google Agent Tools Today

The lowest-friction starting point for most developers is the Gemini API with function calling. The API is well-documented, the SDKs are available in Python, JavaScript, and Go, and the function calling interface works reliably for building tool-using agents.

Developers new to Vertex AI Agent Builder can start with the Studio interface, which allows interactive testing of agent configurations before committing to code. The Data Store connector is the feature most worth prioritizing early, since grounding agent responses in actual organizational data is where much of the practical value comes from for enterprise use cases.

The A2A protocol is worth learning now even if you are not immediately building multi-vendor agent systems. Understanding it prepares you for a world where agent interoperability is a basic infrastructure assumption rather than a special-case integration.

Frequently Asked Questions

Is Google Agentspace free?

Google Agentspace is an enterprise product and is not free. It is priced as a Google Workspace add-on. Pricing varies by organization size and contract. A trial period is available for qualifying enterprise accounts.

Does the Gemini agent store your data?

Under standard Workspace terms, Google does not use Workspace customer data to train its general AI models. Enterprise agreements with data processing addendums provide additional guarantees. For sensitive use cases, Vertex AI deployments with VPC Service Controls give the highest level of data isolation.

How does Agent2Agent (A2A) work?

A2A is a communication protocol that defines a standard message format for agent-to-agent interactions. Each agent exposes a card describing its capabilities, inputs, and outputs. Orchestrating agents discover these cards and route tasks accordingly, without requiring custom integration code for each agent pair.

Can you build custom Google agents without Vertex AI?

Yes. The Gemini API is accessible directly, and you can build agents using standard LLM integration patterns without using Vertex AI. However, Vertex AI provides managed infrastructure for deployment, monitoring, and scaling that is worth using for production workloads.

For the foundational concepts behind AI agents, read What Are AI Agents? A Developer's Guide for 2026.

To see how Google's stack compares to other options, read Best AI Agents for Developers in 2026.

For the industry-wide context in which these products launched, read AI Agents News: The Biggest Developments in 2026.

Use our Hash Generator when working with API authentication for Vertex AI and Gemini API requests, particularly for HMAC-signed webhook payloads.