Google's new Gemini agent is designed to coordinate ongoing work across business applications, employees, and other AI agents, rather than simply respond to individual requests. For businesses, that raises a decision beyond which AI model to use: which system should manage the organizational knowledge, responsibilities, and workflows that allow AI to carry out work over time?

Introduced at Gemini at Work 2026, the agent combines familiar AI capabilities, including answering questions, creating content, and writing and executing code, with the ability to maintain context across different assignments. Instead of starting over each time an employee moves between applications or devices, Gemini is designed to retain information about the work already underway. It can also delegate parts of complex assignments to temporary specialized agents, while organizations can create persistent AI coworkers with continuing responsibilities for a team.

Google is extending that approach by separating the agent coordinating the work from the AI models performing it. The company describes an architecture that can use both its Gemini models and Anthropic's Claude, although third-party model selection remains in early access. This could allow businesses to choose different models for different tasks without rebuilding the context, tools, and workflows supporting those assignments.

The new Gemini agent is currently in private preview for selected business customers, rather than automatically available to everyone using Gmail or Google Workspace. Google's goal is to make Gemini the continuing system through which organizations assign, organize, and execute work, even as the underlying AI models change. Many of the capabilities supporting that approach were already in place. What's different now is how Google is bringing those separate pieces together into one experience.

Google's new Gemini agent is a persistent enterprise AI system designed to coordinate ongoing work across employees, business applications, and other AI agents while maintaining context between assignments.

Google had been building toward this unified experience throughout 2026. In April, the company expanded Gemini Enterprise with capabilities that allowed employees to assign and manage work extending beyond individual conversations. These included long-running AI agents and an Inbox where employees could monitor their progress. Shared Projects also gave employees and agents a common place to collaborate, keeping information and project history available as work continued.

That same month, Google introduced Gemini Enterprise Agent Platform, which serves a different purpose. While the Gemini Enterprise app gives employees a place to work with AI agents, Agent Platform provides the infrastructure businesses need to develop, coordinate, and govern them. Google expanded that foundation in July by making four capabilities more widely available: Agent Runtime, Agent Memory Bank, Agent Identity, and Agent Gateway. Agent Runtime allows agents to continue working on lengthy assignments, while Agent Memory Bank helps them retain context from earlier interactions. Agent Identity establishes an agent's identity, while Agent Gateway provides controls over how agents communicate with tools and other systems.

These developments gave Google much of the infrastructure needed to support AI that works over time. Gemini Enterprise also supports reusable skills, which employees can use with its AI assistant to follow established procedures for recurring assignments rather than starting from scratch each time.

Meanwhile, Google was also extending Gemini's ability to work across everyday business applications. In September, it announced new Google Workspace capabilities that allowed Gemini to use information from emails, documents, and conversations to create presentations, spreadsheets, and other work products. For example, an employee could ask Gemini in Google Chat to prepare a presentation using information from an ongoing project, or turn a lengthy email discussion in Gmail into a structured document in Google Docs. This reduced the need to manually collect and transfer information between applications before asking AI to complete an assignment.

The new Gemini agent brings these capabilities into one experience for conversational assistance, knowledge work, coding, and delegated assignments. Google says employees will be able to access it through web browsers, mobile devices, desktop computers, and command-line tools, as well as applications including Google Workspace, Microsoft 365, and Slack. Because the agent operates in the cloud, longer assignments can continue after an employee closes their computer. Tasks can also be scheduled or triggered by events, allowing some work to begin without a new request. Google identifies multi-step background task delegation, mobile and desktop access, and third-party AI model selection as capabilities that remain in early access.

By connecting these previously separate functions, Google is attempting to reduce the fragmentation that occurs when different AI assistants and applications each manage only part of an assignment. The goal is to maintain continuity as work moves between tools, activities, and people, without requiring employees to repeatedly supply the same information or instructions. That becomes particularly important when an AI agent is no longer helping just one employee complete a task, but is assigned an ongoing responsibility that an entire team depends on.

Google's new Gemini agent introduces a different way for businesses to assign ongoing responsibilities to AI. Instead of each employee using an assistant for individual tasks, organizations can create persistent AI coworkers with defined roles that serve an entire team. These agents remain available across different assignments and conversations, rather than being created for just one task. Google says employees can describe a responsibility, such as an event coordinator or financial analyst, and Gemini will create an agent to take on that role.

These AI coworkers can also have their own presence within the workplace. Google says each AI coworker can receive a dedicated Google Workspace account, complete with an email address, calendar, Drive storage, and a listing in the company's directory. Employees can then work with the agent through familiar tools, such as adding it to a Google Chat conversation or mentioning it directly to assign a task. This gives teams a way to direct work to the same AI coworker rather than relying on separate conversations with individual assistants.

Google illustrates the concept with an event coordinator agent helping prepare for a product launch. A marketing manager could ask the agent in a group chat to draft a launch-readiness document, which it would then share with the team. Colleagues could comment on the document and ask the agent to suggest changes or respond to questions. Its contributions would appear under its own identity in the document's version history, making it possible to distinguish the agent's work from changes made by employees.

These AI coworkers are different from the temporary sub-agents Gemini can create to complete complex assignments. When Gemini receives a task involving multiple steps, it can create specialized sub-agents to handle individual parts of the work, then coordinate their efforts to complete the larger assignment. An AI coworker, by contrast, maintains a continuing role and remains available to the team for different assignments over time.

Google also says each AI coworker operates under its own managed identity rather than impersonating an employee or signing in through that person's Workspace account. Having its own account doesn't automatically give the agent access to everyone's information. Instead, it can work with documents and conversations that team members share with it, following the organization's existing sharing and access permissions.

For businesses, this creates a different way to organize ongoing responsibilities. An AI coworker isn't the equivalent of a human employee, but it could become a shared point of contact for work that continues across multiple projects and team members. To carry out those responsibilities consistently, however, the agent needs more than an identity and an assigned role. It also needs access to the organization's knowledge, tools, and established ways of working.

Google has organized these capabilities around three components: tools, skills, and context. Together, they are intended to help Gemini work with a company's existing systems and follow its established procedures without requiring employees to provide the same information and instructions for every assignment.

Tools connect Gemini to the software and information a business already uses. These connections can include applications such as Microsoft 365 and Slack, customer and service management platforms like Salesforce and ServiceNow, project management tools such as Jira and Confluence, and data systems including BigQuery and Snowflake. Google also supports the Model Context Protocol (MCP), which allows agents to connect with compatible external tools and data sources. Businesses can make their own integrations available through a shared enterprise tools registry, allowing different teams to reuse those connections.

Skills tell Gemini how to perform recurring assignments. For example, a business could create a skill that explains how to prepare a monthly sales report, including which information to collect, which calculations to use, and how the results should be organized. Rather than explaining that process each month, employees could have Gemini follow the established instructions. Google allows organizations to share these skills across departments, while individual employees can also create skills for their own work.

Context allows Gemini to retain information as it completes those assignments. Google divides this capability into four types of memory. Session memory keeps track of an active assignment, even when it takes several days to complete, while semantic memory builds knowledge from documents, conversations, and other information the agent encounters. Procedural memory retains information about how work is performed, including skills Gemini develops, and episodic memory records what the agent has done previously.

These different forms of memory are intended to help Gemini understand the people, information, and procedures involved in its assignments. That becomes useful when a request depends on information an employee hasn't explicitly provided.

For example, Google says an employee could ask Gemini to schedule a meeting with the usual regional event leads without supplying their names. Gemini could identify the appropriate participants from earlier conversations and Google Chat groups, check their calendars, and begin coordinating a meeting.

Gemini can also recognize opportunities to help before an employee explicitly asks it to take on a task. This uses Workspace Intelligence, a technology Google introduced earlier in 2026 to connect information about employees' projects, conversations, and documents across Workspace applications. If a manager sends an email requesting a project update in the form of a presentation, Workspace Intelligence could recognize that request as work Gemini can handle and offer the employee a one-click option to delegate it to the agent.

The difference is that Gemini can use existing workplace information to understand what needs to be done, rather than relying entirely on employees to identify the task and explain its context. When an assignment requires several different activities, Gemini can also delegate parts of the work to temporary sub-agents, coordinating their contributions simultaneously or in sequence.

Google is also applying these same capabilities to more specialized business activities, including financial reporting and data analysis.

Consider a business manager asking Gemini to prepare a report showing the company's net margin for the quarter. Producing an accurate report requires more than finding sales and expense figures. The system needs to know which financial records to use and how the company calculates net margin. Different organizations may follow different accounting practices or use different definitions for internal reporting.

Google's new operational reporting skills are designed to help Gemini handle this type of assignment. BigQuery, Google's cloud-based data analytics service, allows businesses to store and analyze large amounts of information. Knowledge Catalog helps document what that information represents, including the company's established definitions, data structures, and business rules.

By using these systems together, Gemini can identify the appropriate information, apply the company's definitions, and generate the query needed to produce the report. Gemini can save that query so employees can run the report again without paying additional AI token costs to generate the instructions a second time.

This could be particularly valuable for recurring reports. Employees wouldn't need to rebuild the calculations every month, and different departments could use the same established definitions when reviewing company performance. Google is also introducing technical skills for data engineers that can generate PySpark code, prepare notebooks, train machine learning models, and troubleshoot problems in data pipelines.

The importance of company-specific knowledge becomes even more apparent in industries where financial decisions, legal requirements, and confidential information affect how work must be performed.

Google's new Gemini for Financial Services, currently in preview, includes more than 50 foundational skills for activities such as investment research, credit analysis, and risk modeling. These assignments often require information from several sources, including financial markets, regulatory filings, and a company's internal records. Gemini can work with data from providers including FactSet, LSEG, and S&P Global, as well as SEC filings and proprietary company information.

Because financial professionals need to evaluate the information behind an analysis, Google says the system provides confidence scores, documented methodologies, source citations, and data lineage showing where the underlying information originated. These features are intended to help users review the analysis rather than relying solely on the results Gemini produces. Google identifies CME Group and Deutsche Bank as organizations using the specialization.

The legal industry presents a different challenge. Law firms routinely handle confidential information that may only be accessible to attorneys working on a particular case. Gemini Enterprise for Legal, also in preview, is designed to respect these restrictions through connections with document-management systems such as NetDocuments and iManage. Google is also working with Harvey on legal workflows and Onit on contract management.

One example comes from the law firm Cooley, which is developing an agent to identify and redact confidential information, including personally identifiable information, before legal documents become public. The assignment requires the agent to work with sensitive documents while following rules about what information can be disclosed.

These examples illustrate why Google's approach goes beyond giving Gemini access to more applications. A useful AI coworker needs to understand which information is relevant, follow the procedures associated with that work, and apply the organization's established rules. By retaining those connections and instructions, Gemini could make recurring business processes easier to manage and reuse across teams.

Once a business establishes those workflows, however, another question becomes important: does it need to rely on the same underlying AI model to perform every assignment?

Google is designing Gemini so the agent coordinating an assignment and the AI model performing the work can be treated as separate parts of the system. The agent maintains the company's tools, skills, memory, and organizational context, while the underlying model provides the intelligence needed to analyze information, generate content, or write code. This would allow businesses to use different AI models without rebuilding the instructions and connections that support their existing workflows.

For example, a finance team might use Gemini to prepare monthly reports following the procedures we described earlier. Those procedures would remain part of the agent's working context, including where to find the company's financial records, how particular measurements are calculated, and how reports should be organized. The team could then use different supported AI models to complete parts of that work without having to recreate those instructions each time.

Google says this architecture is designed to coordinate assignments across its own Gemini models and Anthropic's Claude, with plans to support additional proprietary and open models. However, that flexibility is still being introduced. Google's current product documentation identifies its Gemini models as available in the existing experience, while the ability to select third-party models remains in early access.

The reason for offering multiple models comes down to their different capabilities and operating costs. A relatively simple assignment, such as preparing a short summary from an existing report, may not require the same processing power as analyzing a complex set of documents or writing and testing software. More capable models can also be more expensive to operate, particularly when agents perform many tasks throughout the day.

Google's Smart Routing system is designed to address this by automatically directing work to the model that provides the appropriate combination of performance and cost. For larger assignments, Gemini could also coordinate different models to complete individual parts of a project.

This approach could help organizations manage AI expenses as agents take on more ongoing responsibilities. Google also describes project-level spending limits that allow administrators to set a maximum amount for AI usage. When a project reaches that limit, the associated agent pauses until an administrator chooses to resume its work. Google had introduced spending caps earlier in 2026, but connecting those controls to ongoing agent activity becomes particularly important when assignments can continue running without employees supervising every step.

Using different AI models within one system is not unique to Google. Other enterprise AI platforms already support routing work between models. What's significant about Google's approach is how it connects that flexibility to the persistent agent managing an organization's work.

As AI models improve and new options become available, businesses could change the models performing particular assignments while keeping their established Gemini workflows in place. That could reduce the need to rebuild processes whenever a better or less expensive model becomes available.

That flexibility also raises a different question. If businesses can change the AI models performing their work while keeping their established workflows intact, who controls the system maintaining those workflows, along with the tools, memory, and organizational knowledge supporting them?

As Gemini takes on more responsibility for coordinating work, businesses need to determine how much authority their AI agents should have. An agent that can access customer records, financial information, and project documents across multiple applications could save employees considerable time. However, that access also makes it important to establish clear rules about what the agent can see, what actions it can take, and how those actions are monitored.

Google's approach allows organizations to connect the business applications they already use without necessarily moving their information into Google products. A company could continue using Salesforce for customer records, Microsoft 365 for documents, and other systems for managing its operations, while Gemini coordinates assignments across them. This means businesses need security controls that extend beyond Gemini itself to the external applications and information the agent uses.

Google says each agent receives its own cryptographically verified identity, allowing organizations to distinguish its actions from those of employees and other agents. Administrators can assign permissions based on the agent's responsibilities, limiting which systems and information it can access. When connecting to external applications, Gemini can use established authorization standards such as OAuth, which allows applications to grant limited access to information and services without sharing user passwords. Its actions are also recorded in audit logs under the agent's identity, including when it creates a virtual machine to run code. This gives administrators a record of what the agent did and which systems it accessed.

For example, an AI coworker responsible for preparing customer proposals might need access to sales records and product information. That responsibility wouldn't require access to employee payroll files or other confidential documents. Google says its security architecture includes Agent Sandbox, which provides isolated environments for agents to execute tasks, and Agent Gateway, which controls communications between agents and the systems they use.

Agent Gateway also allows administrators to establish policies that apply across multiple agents. Google gives the example of a company prohibiting agents from opening documents classified as "Need to Know." Rather than configuring that restriction separately for every agent, administrators could create a policy that applies throughout the organization.

These controls become particularly important when agents can perform actions across several connected systems. The Open Worldwide Application Security Project (OWASP), an organization that develops cybersecurity guidance, identifies several risks specific to autonomous AI agents in its 2026 framework. These include agents misusing tools, gaining access beyond their assigned permissions, and errors spreading between connected agents.

For example, an agent might incorrectly retrieve information it shouldn't have accessed, or pass an inaccurate instruction to another agent responsible for completing the next step of an assignment. When multiple agents can act on one another's information, an initial mistake could affect systems beyond the one where it originated. The ability to coordinate work across applications makes controlling those interactions an important part of deploying AI agents safely.

Google is also developing tools to help organizations identify potentially inappropriate behavior after an agent begins working. In September 2026, Google made Agent Anomaly Detection available in private preview for Gemini Enterprise Agent Platform. The system examines an agent's execution records and tool calls to identify unusual activity, including behavior that might not produce an obvious error.

For example, Google describes an inventory agent that repeatedly retrieves large amounts of information in a way that could indicate unauthorized data collection. Agent Anomaly Detection can identify the unusual pattern and generate a finding for administrators to investigate. This is a separate Agent Platform capability, however, and Google has not established that it is automatically included with the new universal Gemini agent.

Centralizing security controls could make it easier for businesses to apply consistent policies and monitor AI activity across departments. At the same time, the more applications an agent can access and the more assignments it can handle independently, the more important those controls become. Businesses still need to decide which permissions are appropriate, monitor agent behavior, and determine when human approval should be required.

There is also a longer-term consideration that extends beyond security: what happens if a business eventually wants to move its AI workflows to another provider?

Google says Gemini can work with information stored in a company's existing business systems without requiring all of that data to be moved to Google. However, keeping the original information accessible is different from being able to transfer everything an agent has learned about how the organization works. Over time, Gemini could accumulate valuable knowledge through its memory, established procedures, and connections to employees and applications.

Google describes reusable skills and connections to external applications, but its published announcements do not establish how an entire persistent AI coworker could be transferred to another provider while preserving its accumulated context, working history, and responsibilities.

That distinction matters because a company's dependence on an AI platform could increase as more employees and agents begin relying on its established processes. Even if businesses can choose among competing AI models, the system maintaining their knowledge, procedures, and agent responsibilities may become more difficult to replace.

Google is entering a market where several major AI companies are developing systems that can take on ongoing responsibilities rather than simply respond to individual requests. Microsoft, OpenAI, Anthropic, Meta, and SpaceXSI are all pursuing versions of this approach, although they differ in how their agents connect to business applications, retain information, and work with employees.

Microsoft is one of Google's closest competitors because it already provides many of the applications businesses use to manage their daily work. In September, Microsoft introduced an updated Copilot experience that brings conversational assistance, delegated assignments, and software development into a more unified system. Its Cowork capability allows employees to assign complex projects, while the new Autopilot agent is designed to continue working independently with its own identity, memory, and workspace.

Microsoft's Microsoft IQ platform helps Copilot and its agents understand how a business operates by connecting information about employees, projects, conversations, and business systems. For example, an employee preparing a customer proposal could have Copilot draw information from previous sales conversations, customer records, and support tickets rather than collecting those details manually. Microsoft's Agent 365 provides centralized management and governance for agents, including those developed by other providers. Like Google, Microsoft supports selecting different AI models for different assignments.

OpenAI is pursuing a similar goal through Dots, persistent AI agents introduced in September that can work across connected applications and continue making progress between interactions. Each dot has its own cloud computer and can retain information about a user's responsibilities and preferences. OpenAI is also testing specialist dots with businesses, allowing organizations to create agents with their own identities, access permissions, and continuing responsibilities. These specialist agents are currently being developed through focused enterprise pilots.

Anthropic is extending Claude's ability to participate in team workflows through Claude Tag, building on products such as Claude Cowork. Claude Tag allows employees to bring Claude into Slack conversations by mentioning it directly, giving teams a shared AI participant that can work with connected tools and information. Within a channel, Claude can retain relevant context, follow ongoing work, and respond to requests from different team members. This gives teams a shared AI participant that can contribute to projects without requiring each employee to start a separate conversation.

SpaceXSI's Grok Bot offers another approach to persistent AI coworkers. Its agents operate through dedicated cloud computers and can interact with applications while continuing assignments after employees leave their devices. The company's Team Bots offering extends that capability to groups, allowing employees to share agents configured with common files, skills, and business knowledge. For example, a sales team could use a shared bot to review customer information, prepare daily account updates, and retain context as different employees work with the same client.

Meta is approaching the market from a more consumer-oriented starting point. Its Muse agent was introduced to help individuals complete tasks across websites and applications, while Muse for Small Business extends those capabilities through connections to business accounts and tools. Business owners can use Muse to review marketing performance, examine financial information, and prepare content. Meta says the agent requires approval before taking actions such as publishing, sending messages, or spending money.

Google's approach combines its Workspace applications, Google Cloud infrastructure, business data connections, and persistent AI coworkers within one coordinating agent. Its proposed ability to work with competing AI models adds another element, although Microsoft and other providers are also developing ways to select models according to the assignment.

For businesses evaluating these products, however, there's an important distinction between what companies have announced and what customers can currently use.

Google introduced its new universal Gemini agent in private preview on October 8, 2026, for selected business customers. The company has not announced a general-availability date. Although many of the individual services supporting Gemini are already available through Gemini Enterprise and Google Workspace, the complete experience described at Gemini at Work 2026 is not yet broadly accessible.

Google's product documentation identifies several capabilities that remain in early access, including multi-step background task delegation, mobile and desktop functionality, and the ability to choose third-party AI models. Businesses already using Gemini Enterprise should therefore not assume that every capability demonstrated during the announcement is included in their existing service.

Google told VentureBeat that the new agent will be included for eligible Gemini Enterprise customers without a separate agent fee when it becomes more widely available. However, the company has not fully explained how persistent coworker accounts will be licensed or what additional usage charges might apply to long-running assignments and sub-agents. Those details will be important for businesses considering agents that work continuously rather than completing occasional requests.

Google is marketing the new agent to small and midsize businesses as well as larger organizations. Smaller companies could potentially benefit from assigning recurring responsibilities to AI without building separate systems for every task.

Google has reported substantial adoption of its broader Gemini Enterprise ecosystem, including deployments involving thousands of agents and significant productivity improvements. Those results demonstrate how customers are using Google's existing AI products, but they do not establish how the newly introduced universal agent performs. Google's announcement does not provide independent comparative results showing how reliably the new agent performs across lengthy assignments involving multiple applications.

As these systems become more widely available, businesses will have more opportunities to evaluate how reliably agents complete work, how much they cost to operate, and how well they fit existing processes.

Google's new Gemini agent could change what businesses are committing to when they adopt AI. For organizations planning to use AI coworkers for ongoing responsibilities across teams and applications, Gemini could become the system that keeps track of how that work is done, rather than simply completing individual assignments. As that system takes on more responsibilities, the organizational knowledge and established procedures it maintains could become increasingly valuable to the employees and agents who rely on them to keep work running.

For businesses, that creates a trade-off. Google's approach could allow organizations to use different AI models without rebuilding the workflows supporting their assignments. However, if a company later decides to move those workflows from Gemini to a competing platform, such as Microsoft's, the transition may not be as simple as switching AI models. Even if the company's original data remains accessible, it could need to recreate its agents' accumulated memory, instructions, permissions, and established processes. That could make changing platforms more time-consuming, expensive, and disruptive as those agents take on more responsibilities.

These considerations are particularly important because Google's new agent remains in private preview. Its performance across complex assignments, full operating costs, and the ability to transfer an entire AI coworker to another provider have not yet been established through broader deployment.

As Google and its competitors develop similar systems, the competition could increasingly extend beyond the capabilities of individual AI models to the platforms that coordinate workplace activity. Businesses may find that choosing which system will maintain their organizational knowledge, responsibilities, and workflows is just as consequential as choosing the AI models that perform the work.

Q: What is Google's new Gemini agent?
A: Google's new Gemini agent is a persistent enterprise AI system designed to coordinate ongoing work across employees, business applications, and other AI agents. It combines conversational assistance, coding, organizational context, and delegated task execution in one experience, allowing businesses to manage assignments that continue across multiple interactions and applications.

Q: How is the new Gemini agent different from existing Gemini tools?
A: Google's new Gemini agent brings previously separate enterprise AI capabilities into one coordinating system. While earlier Gemini products supported individual tasks, long-running agents, and work across applications, the new experience is designed to maintain continuity across those activities and coordinate persistent AI coworkers, temporary sub-agents, and potentially different underlying AI models.

Q: What are Gemini AI coworkers, and what can they do?
A: Gemini AI coworkers are persistent agents assigned continuing responsibilities for a team, such as coordinating events or supporting financial analysis. Each coworker can have its own Google Workspace identity, including an email address, calendar, Drive storage, and directory listing. Employees can assign work through Google Chat and collaborate with the same agent across multiple projects.

Q: How does Gemini remember information and work across business applications?
A: Google's Gemini agent uses connected tools, reusable skills, and four types of memory to support ongoing assignments. Tools provide access to authorized business systems, skills define established procedures, and session, semantic, procedural, and episodic memory help maintain information about active work, organizational knowledge, processes, and previous actions. These capabilities reduce the need to repeatedly provide the same instructions and context.

Q: Can Google's Gemini agent complete tasks without someone constantly prompting it?
A: Google's Gemini agent is designed to continue assignments in the cloud after employees close their computers. It can coordinate temporary sub-agents, and planned capabilities include scheduled or event-triggered tasks. Workspace Intelligence can also recognize potential assignments from workplace communications and suggest delegating them to Gemini. Some background delegation capabilities remain in early access.

Q: Can Google's Gemini agent use Claude or other AI models?
A: Google's new Gemini agent is designed to coordinate work across Google's Gemini models and Anthropic's Claude, with additional models planned. Its architecture separates the persistent agent managing tools, memory, and workflows from the AI models performing individual tasks. This could allow businesses to select models according to performance and cost, although third-party model selection remains in early access.

Q: How does Google keep Gemini AI coworkers secure?
A: Google's enterprise agent architecture includes managed agent identities, access permissions, audit logs, isolated execution environments, and centralized security policies. These controls are designed to limit what agents can access, distinguish their actions from those of employees, and help administrators monitor activity across connected systems. Organizations must still configure appropriate permissions and oversight, and not every Agent Platform security capability is confirmed as a default feature of the new universal agent.

Q: Can businesses move their Gemini AI coworkers to another AI platform?
A: Google supports connections to external business systems and reusable agent components, but its published documentation does not establish a complete method for transferring a persistent Gemini AI coworker to another provider with its accumulated memory, history, permissions, and responsibilities intact. Businesses may therefore need to consider workflow portability separately from their ability to change underlying AI models.

Q: How is Google's Gemini agent different from Microsoft Copilot and other AI agents?
A: Google's Gemini agent combines Workspace applications, Google Cloud infrastructure, persistent AI coworkers, and planned multi-model orchestration within one coordinating experience. Microsoft's Copilot ecosystem offers overlapping workplace intelligence and agent-management capabilities, while OpenAI, Anthropic, Meta, and SpaceXSI are developing different approaches to persistent agents and shared workflows. The distinction lies in how each provider connects organizational knowledge, tools, and ongoing responsibilities.

Q: Is Google's new Gemini agent available to businesses now?
A: Google's new universal Gemini agent entered private preview for selected business customers on October 8, 2026. Google has not announced a general-availability date. Although several supporting capabilities already exist in Gemini Enterprise and Google Workspace, the complete new experience is not broadly available, and features such as third-party model selection and background task delegation remain in early access.

Q: How much will Google's Gemini AI coworkers cost?
A: Google told VentureBeat that the new Gemini agent will be included for eligible Gemini Enterprise customers without a separate agent fee when it becomes more widely available. However, Google has not fully detailed how persistent AI coworker accounts will be licensed or what additional usage charges may apply to long-running assignments and sub-agents. The full operating costs remain unclear.

Editor’s Note: This article was created by Alicia Shapiro, CMO of AiNews.com, with writing support, AEO/GEO/SEO optimization, image concept development, and editorial structuring support from ChatGPT, an AI assistant. All final editorial decisions, perspectives, and publishing choices were made by Alicia Shapiro.