Google Agentspace: The AI Platform That Transforms Enterprise Workflows

If you’ve worked in any modern company, you know the pain: information silos, constant application switching, and disjointed workflows that bleed productivity. This is the precise problem Google is solving with Google Agentspace.

Announced in early 2025 and now the core agentic platform within Gemini Enterprise, Agentspace represents a crucial shift in enterprise AI. It is not just an assistant; it is a unified AI execution layer designed to act as a true digital coworker that understands your tools, manages the chaos, and completes complex, multi-step tasks.

At SquaredTech, we’ve tracked this platform from day one. This comprehensive guide breaks down what Google Agentspace is, details its advanced features, shows you the smart way to implement it, and explains why it’s the top cross-platform choice for enterprise workflows in 2026.

Read More About Our Article of Google Agentspace and MidJourney V7 Explained: AI Agents and Image-to-Video Revolution Published on October 3, 2025 SquaredTech

What is Google Agentspace?

Google Agentspace is a comprehensive, permission-aware platform that combines Gemini’s most advanced multimodal AI models with enterprise-grade security and your company’s private data.

Its primary purpose is to move AI beyond simple Q&A into active task execution across disconnected enterprise systems.

Powered by Gemini, Agentspace supports multimodal reasoning, meaning it can ingest, plan, analyze, and execute actions based on information from:

Text and Conversations

Images and Visual Data

Structured Data (spreadsheets, databases)

Unstructured Data (PDFs, documents, Slack messages)

For organizations, this means employees gain a system that doesn’t just summarize documents, but actively completes actions—like updating a Salesforce record after analyzing a contract in Google Drive. This significantly reduces wasted time and fundamentally changes how cross-platform workflows are managed.

Agentspace vs. Gemini: What is the Difference?

Gemini is the engine (the Large Language Model, or LLM).

Agentspace is the factory floor (the platform, the connectors, and the execution layer) where Gemini’s intelligence actually performs tasks across your enterprise tools.

Essential Key Features of Google Agentspace Platform

Google Agentspace introduces several innovations that set it apart from other AI platforms by directly addressing the complex needs of large organizations.

Comprehensive Tool Integration

Agentspace serves as the central enterprise AI hub. It connects with critical tools like Slack, Salesforce, ServiceNow, Jira, Confluence, Google Drive, and Microsoft SharePoint. This direct integration removes the friction of application switching.

Active Collaboration with Users

Unlike static automation, Agentspace agents are designed to interact dynamically. They ask for clarifications, validate assumptions against real data, and seek mandatory approval before finalizing outputs, preventing errors caused by missing context.

Complex Task Execution

Agents are capable of handling demanding, multi-step workflows. They can update CRMs, manage project trackers, perform deep account research, and plan multi-step processes without constant human oversight.

True Multimodal Data Processing

The platform supports multiple data types. From spreadsheets and PDFs to images and Google Drive searches, Agentspace processes them seamlessly. Employees no longer need separate tools for handling varied content formats.

Prebuilt Expert Agents

Google provides specialized agents for immediate productivity:

• Deep Research Agent: Conducts hundreds of searches, compiling detailed reports.
• Idea Generation Agent: Deploys multiple AI agents to brainstorm and refine creative solutions.
• NotebookLM Plus: Synthesizes enterprise knowledge to discover new insights and connections.

These agents accelerate adoption by offering instant, powerful use cases right out of the box.

Agent-to-Agent (A2A) Communication

This forward-looking feature allows agents from different providers and frameworks (a Vertex AI agent talking to an Agentspace agent, e.g.) to interact, collaborate, and share data, enabling organizations to build highly flexible, hybrid AI ecosystems.

Agentspace Pricing: Understanding the Gemini Enterprise Tiers

Enterprise AI pricing is a major consideration. Agentspace functionality is tied into the Gemini Enterprise subscription tiers.

Tier	Monthly Cost (Per User)	Core AI Capability	Key Feature Included
NotebookLM Enterprise	$9 USD	Knowledge Synthesis, Summarization	Research grounded in your own uploaded documents and data.
Gemini Enterprise Standard	$25 USD	Enterprise Search, Data Discovery	Permissions-aware search across all integrated enterprise data.
Gemini Enterprise Plus	$45 USD	Full Agent Creation & Execution	No-Code Agent Designer, Custom Agent Deployment, and higher quotas.

Cost Advisory: The subscription covers access and core features. Complex agent tasks that involve heavy computation (e.g., large-scale data analysis, multi-step orchestration) may incur additional, usage-based cloud resource costs on Google Cloud. Organizations must model the combined cost for accurate budget forecasting.

Practical Use Cases for Enterprises

Google Agentspace is not limited to one department. Its flexibility makes it a powerful tool across the entire organization.

What is the most common real-world use case for Google Agentspace?
The most common and highest-value use case is Internal Knowledge Hub and Unified Search. Agentspace is deployed to provide a single, permissions-aware interface that synthesizes information, summarizes documents, and answers employee questions instantly across all disparate systems (like Confluence, SharePoint, and Slack history)—effectively breaking down information silos.

Department	Agent Task / Workflow Example	Business Value
Marketing	Campaign Analysis Agent: Pulls data from spreadsheets (Google Sheets), summarizes performance trends, and drafts an SEO optimization brief in a single action.	Accelerates content creation; shifts analyst focus from data compilation to strategy.
Finance & Law	Contract Review Agent: Analyzes a PDF contract against internal policy documents, flagging high-risk clauses and summarizing key regulatory compliance points.	Automates due diligence; minimizes human error in financial reporting.
Human Resources	Onboarding Assistant: Answers employee benefit and policy questions instantly by querying documents in SharePoint and Slack history.	Reduces HR administrative burden; streamlines new hire onboarding process.
Technology & IT	Ticket Triage Agent: Reads incoming ServiceNow or Jira tickets, checks related documentation in Confluence, and automatically assigns the ticket to the correct expert or team based on topic and severity.	Increases operational efficiency; improves issue resolution speed.
Sales Operations	CRM Update Agent: Reads summary notes from a Zoom meeting transcript and uses the information to update the corresponding contact and opportunity fields in Salesforce.	Ensures data integrity; removes manual data entry burden from the sales team.

How to Get Started: The 3-Phase Adoption Strategy

SquaredTech recommends a phased adoption to secure immediate wins and ensure organizational change management is successful.
How are AI agents created in Google Agentspace?
AI agents can be created in two ways: through a no-code interface for business users, and through a developer kit for engineering teams.

No-Code Agent Designer (Business Users): Available with Gemini Enterprise Plus. Users describe the agent’s instructions, define data sources, and orchestrate multi-step workflows using natural language and a visual canvas—no programming skills required.

Vertex AI Agent Development Kit (Developers): For engineering teams building highly custom agents, integrating with specialized APIs, and managing complex logic using code.

Phase 1: High-Impact Validation

• Goal: Prove the platform’s value quickly.
• Action: Deploy prebuilt agents like Deep Research in high-value departments (e.g., Legal, Strategy). Enable the Chrome integration for easy access.

Phase 2: Workflow Democratization

• Goal: Enable non-technical staff to automate repetitive tasks.
• Action: Train business teams on the No-Code Agent Designer to build simple, localized agents for tasks like: HR policy lookups or expense categorization.

Phase 3: Operational Integration

• Goal: Embed Agentspace into the core business process.
• Action: Utilize the Vertex AI Agent Development Kit to create highly customized, complex, cross-functional agents that integrate with specialized legacy systems.

Agentspace vs. Copilot vs. AWS Q: The Enterprise Agent Showdown

Platform	Core Enterprise Anchor	Agentspace’s Competitive Edge
Google Agentspace	Cross-Platform Workflows & Multimodal Data (Gemini)	Best for unifying fragmented SaaS environments and excelling at tasks requiring chart/spreadsheet analysis.
Microsoft Copilot Studio	Microsoft 365 Ecosystem (Office, Teams, Azure)	Seamless inside M365. Lacks the native, deep multimodal reasoning and the independent, open A2A protocol of Agentspace outside the Microsoft perimeter.
Amazon Q Business	AWS Cloud Infrastructure & Document Data Lakes	Ideal for AWS-native organizations. Offers strong data indexing but has a steeper learning curve for non-developers compared to Agentspace’s no-code builder.

Is Google Agentspace a replacement for Microsoft Copilot?

No, Google Agentspace is generally not a direct replacement; it is a specialized competitor with a different focus. Copilot excels at tasks within the Microsoft 365 ecosystem (Outlook, Teams, Excel). Agentspace excels at orchestrating complex, multi-step workflows across different platforms, including Microsoft, Salesforce, Jira, and Google Workspace. For companies with diverse SaaS tool stacks, Agentspace is the more complete solution.

Security and Data Protection in Agentspace

Security is non-negotiable for enterprise AI adoption. Agentspace is built on Google Cloud’s secure infrastructure with two core principles:

What data does Google Agentspace use for training its AI?

Google Agentspace uses your enterprise data for grounding, not for model training. Your private company data (including prompts, outputs, and any information connected via data stores) is never used to train Google’s foundation models (like Gemini) or any models for other customers. Data is used exclusively to provide accurate, grounded answers and to execute tasks for your organization.

Permission Inheritance: Agents obey existing enterprise access policies. They cannot access data (in Drive, Salesforce, or Jira) that the user who invoked them is not already authorized to see.

Compliance Framework: Built-in safeguards, including PII/PHI scanning, role-based access control (RBAC), and compliance certifications (GDPR, HIPAA, ISO), ensure data sovereignty and protection.

Conclusion: The Future of AI Workers with Agentspace

Google Agentspace signals a major transformation in enterprise AI. By uniting Gemini’s powerful multimodal reasoning, deep cross-platform integration, and strong enterprise governance, it gives businesses the ability to eliminate information silos and automate complex, multi-step workflows.

Squaredtech believes companies adopting Agentspace today will gain a measurable competitive edge. Its growing ecosystem of expert agents, continuous updates, and customization options are laying the foundation for a workplace where intelligent agents collaborate seamlessly with humans across all critical functions.

Originally published on October 5th, 2025, this post has been updated with the latest information and analysis for the 2026 edition.

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