OpenAI Workspace Agents vs Google Gemini Enterprise: Complete Comparison 2026

What You'll Learn

• What OpenAI Workspace Agents and Gemini Enterprise Agent Platform are designed to do
• How their building, deployment, governance, and team-use layers differ
• What the official pages actually say about availability and pricing
• How to compare the platforms without treating vendor examples as independent performance proof

OpenAI Workspace Agents vs Google Gemini Enterprise is not a simple model-versus-model contest. The official OpenAI announcement describes shared agents that teams create in ChatGPT, connect to tools, and use across ChatGPT or Slack. Google Cloud describes Gemini Enterprise Agent Platform as an enterprise platform for building, deploying, governing, and optimizing agents, with Agent Studio, the Agent Development Kit, runtime services, memory, identity, and observability.

Both pages carry April 22, 2026 as the announcement date. OpenAI frames Workspace Agents around repeatable team workflows and shared organizational context. Google frames Agent Platform around the full engineering and operating lifecycle for enterprise-grade agents. The overlap is real, but the products are not presented at exactly the same layer.

This article uses the official OpenAI announcement, the OpenAI Academy guide, the Google Cloud launch post, and the Google Cloud documentation overview. It replaces the old article's unsupported winner language, exact savings claims, and unverified product equivalence with a source-bounded comparison. For related context, see our AI agent security and governance guide and our multi-agent architecture guide.

What Changed on April 22, 2026

OpenAI announced Workspace Agents as Codex-powered agents for teams. The announcement says teams can create shared agents that handle complex tasks and long-running workflows within organizational permissions and controls. It describes agents that can prepare reports, write code, respond to messages, gather context, follow team processes, ask for approval, and keep work moving across tools.

Google Cloud announced Gemini Enterprise Agent Platform as a full platform to build, scale, govern, and optimize agents. Google describes it as an evolution of Vertex AI that brings model selection, model building, and agent building together with agent integration, DevOps, orchestration, and security capabilities.

What Are OpenAI Workspace Agents?

OpenAI says Workspace Agents are an evolution of GPTs powered by Codex. They run in the cloud and can be shared within an organization. The announcement describes agents that can work in ChatGPT or Slack, use connected applications, write or run code, remember what they have learned, and continue across multiple steps.

The product is built around a team workflow. A user can click Agents in the ChatGPT sidebar, describe a job the team performs often, and follow a guided process to turn that description into an agent. The Academy guide explains the underlying idea as a trigger, a process with skills, and tools or systems that the agent can use.

OpenAI's examples include software review, product feedback routing, weekly metrics reporting, lead outreach, and third-party risk management. These examples show the intended range of workflows. They are vendor examples rather than independently audited proof that every organization will receive the same result.

The official announcement also says teams can create templates, connect tools, add skills, test the workflow, and share the result. This makes Workspace Agents especially relevant when the problem is repeated coordination across a team rather than only model access for a developer.

What Is Gemini Enterprise Agent Platform?

Google Cloud describes Gemini Enterprise Agent Platform as a unified platform for building, deploying, governing, and optimizing enterprise-grade agents and model-based solutions. Its documentation says the platform supports the complete AI lifecycle, from accessing more than 200 foundation models to deploying and managing agents.

The platform combines low-code and code-based routes. Agent Studio provides a visual canvas for designing and prototyping agents. The Agent Development Kit is described as a modular, model-agnostic framework for complex reasoning and tool use. Agent Garden provides prebuilt agents and templates, while Model Garden gives access to Google, third-party, and open-source models.

Google Cloud also describes runtime and memory components. Agent Runtime supports scalable deployments and long-running agents. Sessions manage state within an interaction. Memory Bank supports persistent memory across sessions. Code execution runs Python in a secure sandbox for calculations, analysis, and other logic.

The product therefore reaches beyond a shared assistant surface. It is presented as an operating platform with development, runtime, governance, and quality controls. That breadth is the central distinction to keep in mind when comparing it with Workspace Agents.

Building Agents: Studio, Skills and Codex

OpenAI's official pages emphasize a guided builder. A team describes the job in plain language, selects approved tools and connectors, chooses a trigger, adds guardrails, and tests the result. Workspace Agents can use skills in their instructions, and the announcement says the agent can define steps, connect tools, and be tested until it works as expected.

Google's official pages emphasize multiple development surfaces. Agent Studio offers low-code design. The Agent Development Kit offers code-based construction. Agent Garden supplies starting templates. The documentation also lists a Managed Agents API and an Interactions API for configured agents and runtime interaction.

Tools, Integrations and Team Surfaces

OpenAI says Workspace Agents can use connected apps, files, code, tools, and memory. The announcement specifically describes use in ChatGPT and Slack, with more surfaces coming. It says agents can run on a schedule or be deployed in Slack to pick up requests as they arrive.

Google Cloud's platform is described as connecting agents to internal systems through native ecosystem integrations. The official launch post mentions BigQuery, Pub/Sub, Agent Gateway, Model Context Protocol, and partner ecosystem agents. The documentation lists tools and MCP servers in Agent Registry and describes Agent Gateway as a policy enforcement point for tool calls and authentication.

These are not identical integration claims. OpenAI's page emphasizes where teams interact with a shared agent. Google's pages emphasize how developers and administrators connect, deploy, and govern agents across enterprise environments. A buying team should map the actual systems it needs to connect rather than count product names in a feature list.

Our MCP and enterprise operations article provides related context on tool connectivity. It does not establish that every connector listed by either vendor is available in every plan or region.

Governance, Permissions and Approvals

OpenAI's announcement says organizations control which tools and data an agent can use, which actions it can take, and when it needs approval. For sensitive actions such as editing a spreadsheet, sending an email, or adding a calendar event, an organization can require the agent to ask for permission first.

OpenAI also says Enterprise and Edu administrators can control connected tools and actions for user groups, manage who can use, build, and share agents, and view agent configuration and runs through the Compliance API. The announcement says administrators can suspend agents when needed. It also mentions safeguards for misleading external content and prompt-injection attacks.

Google's documentation describes Agent Identity, Agent Registry, Agent Gateway, Model Armor, governance policies, threat detection, and vulnerability scanning. Agent Identity gives each agent a managed identity for access control and auditing. Agent Gateway is described as a central point for authentication and policy enforcement. These are platform-level controls rather than a promise that a deployment is automatically compliant with every regulation.

Security should therefore be evaluated as an operating design. Ask who can create an agent, which data it can read, which actions require approval, how runs are logged, how credentials are managed, how incidents are investigated, and how an agent is disabled. Product names alone cannot answer those questions.

Runtime, Memory and Long-Running Work

OpenAI says Workspace Agents run in the cloud and can continue working when a user is away. It describes agents that can work across multiple steps, use memory, run on a schedule, or respond in Slack. The useful unit is a shared workflow that can continue beyond a single chat turn.

Google Cloud describes long-running agents that can maintain state for days through Agent Runtime and Memory Bank. The documentation also describes sessions, persistent memory, code execution, and agent-to-agent orchestration. These components are aimed at deployments that need durable state and operational controls.

Long-running behavior increases the importance of stop conditions, approvals, logs, and data boundaries. Neither official source should be read as a guarantee that an agent will complete every workflow without supervision. The system still needs a well-defined objective, approved tools, and a review path for consequential actions.

Model Access and Platform Scope

OpenAI's announcement presents Workspace Agents as Codex-powered. The agent can write or run code and use connected tools within the ChatGPT workspace context. The official pages focus more on workflow construction and team use than on a public multi-model catalog for the product.

Google Cloud explicitly says Agent Platform provides access to more than 200 foundation models through Model Garden. The launch post names Gemini 3.1 Pro, Gemini 3.1 Flash Image, Lyria 3, Gemma 4, and Anthropic Claude models among the available categories described there. The documentation calls the Agent Development Kit model-agnostic.

The two model statements answer different questions. OpenAI describes the engine and workflow environment for Workspace Agents. Google describes a platform that lets customers choose among a broad model collection. This does not by itself prove that one vendor's models are more accurate, cheaper, or safer for a given task.

Our AI cybersecurity guide can be used for general risk terminology. It is not a benchmark of the products compared here.

Availability and Pricing Evidence

The OpenAI announcement contains a mixed availability statement. Its opening summary says Workspace Agents are generally available in ChatGPT Business, Enterprise, and Edu, while the body and availability section describe research preview access in ChatGPT Business, Enterprise, Edu, and Teachers plans. The safe conclusion is that availability and administrator controls depend on the plan and the current product state.

For pricing, the same official OpenAI page says Workspace Agents are free until May 6, 2026, with credit-based pricing starting on that date. It does not provide a comparable per-seat price for the agent feature in the fetched page. Google Cloud's launch and overview pages explain platform components but do not provide a single comparable price in the source extracts used here.

Do not choose a platform from a price headline alone. A meaningful estimate needs expected runs, model usage, tool calls, storage, runtime, data transfer, administrative work, and any existing contract terms. Those inputs are not supplied by the source pages, so this article does not invent a total cost of ownership.

Use Cases and Vendor Examples

OpenAI gives examples of software review, product feedback routing, weekly metrics reporting, lead outreach, third-party risk management, month-end close, and sales support. It also describes internal examples involving call notes, account research, Slack, CRM updates, and reports. These examples illustrate workflows that combine shared context, tools, and approvals.

Google Cloud gives examples from Burns and McDonnell, Color Health, Comcast, Geotab, Gurunavi, L'Oréal, Payhawk, and PayPal. The page describes uses involving organizational knowledge, screening, customer support, restaurant discovery, expense submission, and agent-based commerce. These are vendor-published customer statements and should be read as illustrative evidence, not as independent comparative testing.

The better comparison is the workflow shape. If the task begins with a team member asking for help inside ChatGPT or Slack, a shared Workspace Agent may fit the interaction pattern. If the task requires a governed runtime, agent identity, deployment controls, evaluation, and integration with enterprise systems, Agent Platform may fit the operating pattern. The final choice depends on requirements that the official pages do not resolve for every organization.

For a separate example of enterprise agent controls, read our AI engineering market article only as background. It does not provide product pricing or performance evidence for this comparison.

Which Platform Fits Which Operating Model?

Workspace Agents are presented as a way for teams to turn repeatable knowledge and processes into shared agents. The strongest fit is a workflow where the team already works in ChatGPT or Slack, needs connected tools, and wants a guided builder with organizational controls and approvals.

Gemini Enterprise Agent Platform is presented as a broader platform for developers and administrators. The strongest fit is a workflow that needs low-code and code-based development, deployment and runtime services, persistent memory, identity, a central agent registry, gateway policies, evaluation, observability, and access to a large model catalog.

This is not a universal winner. A smaller team may value a guided workflow builder more than an extensive runtime stack. A platform team may need the identity, gateway, registry, sandbox, and evaluation capabilities described by Google. An enterprise already standardized on one cloud or collaboration environment may also have integration requirements that change the practical decision.

Start with one repeatable workflow and define a successful output. Identify the systems the agent must read and write, the data it may retain, the actions that need approval, the person who owns the workflow, and the conditions that must stop execution. Test ordinary cases and failure cases rather than only a polished demo.

Ask OpenAI how the intended plan handles workspace-agent access, credit usage after May 6, administrative controls, connected tools, Compliance API visibility, Slack deployment, schedules, and approval requirements. Ask Google Cloud about model and runtime pricing, region, Agent Studio and ADK paths, identity, gateway policies, Memory Bank, evaluation, observability, and support.

Record the answers in a comparison sheet. Vendor pages change, and the OpenAI announcement contains both generally available and research preview wording. A dated product page is useful evidence, but the customer's current account configuration and contract are stronger evidence for an implementation decision.

Evidence Limits and Final Comparison

The official sources support a careful conclusion. OpenAI Workspace Agents are shared Codex-powered agents for repeatable team workflows in ChatGPT and Slack, with tools, memory, schedules, permissions, approvals, analytics, and enterprise controls described on the announcement page. Google Gemini Enterprise Agent Platform is a broader platform for building, deploying, governing, and optimizing agents, with Agent Studio, ADK, runtime, memory, identity, registry, gateway, evaluation, observability, and access to more than 200 foundation models described in official materials.

The sources do not support a universal winner, a guaranteed return on investment, a fixed current price comparison, a claim that one product is safer in every deployment, or a conclusion that the two products are direct substitutes. OpenAI's page gives a free period through May 6, 2026 and later credit-based pricing, but a complete current cost cannot be calculated from the source. Google Cloud's fetched pages do not give a comparable single price.

Use the operating model as the decision frame. Choose the route that matches the team's working surface, required integrations, governance depth, deployment architecture, model strategy, budget method, and human approval process. Run a controlled pilot, measure task success and failure handling, and review the vendor's current commercial and security documentation before committing.