Google Brings Agentic AI to Gemini, Starting With Enterprise Customers

Google has introduced a new agentic AI experience for Gemini, allowing businesses to delegate complex tasks to an AI agent that can plan workflows, use connected applications, write and execute code, and deliver completed work. Announced on October 8, 2026, at Google’s Gemini at Work event, the new Gemini agent is designed to operate across workplace systems through a single interface. Rather than simply answering questions or following individual commands, the agent can be given objectives and determine the steps needed to achieve them. Google is initially focusing on enterprise customers as it develops the security, governance and performance controls required for more autonomous AI systems. Google Cloud’s official announcement outlines the new capabilities.

The launch marks Google’s latest move in the increasingly competitive market for AI agents, where companies are developing systems capable of completing multi-step tasks with limited human intervention. The Gemini agent can connect to Google Workspace, Microsoft 365, Slack, Jira, enterprise databases and other business systems. It can also select an appropriate AI model for a task, with support for Anthropic’s Claude models among the initial options. Google says the goal is to help employees move from requesting information to delegating entire pieces of work, while retaining administrative controls over how the agent accesses company data and performs actions.

What Is Google’s New Gemini Agent?

The Gemini agent is a workplace AI system designed to handle a broader range of tasks than a conventional chatbot. Users can describe an outcome they want, and the agent can plan the work, use specialised tools and return a completed result.

For example, an employee could ask the agent to analyse sales performance, gather relevant information from internal documents, prepare a summary and produce supporting code or reports. Instead of requiring a separate prompt for every step, the system can coordinate the workflow across connected applications.

Google describes the product as a universal agent for work, combining question answering, knowledge work, content creation and coding within a single interface.

The agent is being introduced through Google’s enterprise offering, reflecting the company’s intention to address business requirements such as identity management, permissions, auditability and predictable costs before expanding access more broadly.

Key Features of the Gemini Agent

1. Autonomous task planning

The new system is designed to accept objectives rather than just individual instructions. It can break a task into smaller steps, use available tools and coordinate work across different systems.

For businesses, this could mean delegating research, data analysis, report preparation and other repetitive workflows to an AI agent. Employees can follow its progress and intervene when necessary.

2. Connections to workplace applications

The agent can access information and tools across Google’s own products and third-party platforms, subject to the permissions and integrations configured by an organisation.

Supported systems include:

• Google Workspace applications such as Gmail, Drive, Docs, Sheets, Calendar and Chat.
• Microsoft 365.
• Slack and ServiceNow.
• Jira, Confluence and Git-based development environments.
• Data platforms including BigQuery, Databricks, PostgreSQL and Snowflake.

Google also supports connections to Model Context Protocol (MCP) servers, which can allow AI systems to interact with external tools and data sources through a standardised interface.

These integrations could reduce the need for employees to switch repeatedly between applications, although the agent’s capabilities depend on the specific connection, available permissions and task requirements.

3. Multi-model AI selection

One of the notable features is Google’s approach to model selection. The Gemini agent can automatically choose a model suited to the task, while users can also select a model manually.

Anthropic’s Claude models are among the initial third-party options. Google plans to expand model choice to include additional open-source and private models.

This approach could allow businesses to use different models for different requirements, such as coding, summarisation, analysis or content creation, rather than relying on a single model for every task.

4. Task tracking and audit trails

Google is introducing a task-inbox interface that lets users monitor the agent’s progress. Users can see its activities, including task delegation, the use of specialised skills and code execution.

The system can also maintain an audit trail that attributes actions to the agent rather than incorrectly recording them as actions performed by an individual employee.

This distinction is important in enterprise environments, where companies need to understand what happened, which system performed an action and whether it was authorised.

Gemini Gets Its Own Workplace Identity

Google is also giving the agent a dedicated Workspace identity, including its own email address and context within an organisation.

This enables businesses to interact with the agent in ways that resemble collaboration with a digital team member. Depending on the configured workflow, employees can tag it, email it, share information with it or add it to group conversations.

The agent can use organisational context, such as team structures, calendars, time zones and approval requirements, to help coordinate work.

For instance, an agent assigned to prepare a project update could gather relevant documents, identify the appropriate stakeholders and account for approval processes. However, its access and authority remain dependent on the organisation’s policies and permissions.

Giving an AI agent a workplace identity also creates additional governance requirements. Companies must ensure that it does not gain broader access than necessary and that sensitive actions remain subject to appropriate safeguards.

Why Google Is Starting With Businesses

Google is initially targeting enterprise customers because autonomous AI systems introduce risks that are more complicated than those associated with ordinary chatbots.

An AI assistant that produces an incorrect summary may mislead a user. An agent that can modify documents, run code, send messages or interact with business systems could create operational problems if it misunderstands a task or acts without adequate authorisation.

Google CEO Sundar Pichai highlighted the importance of security, scale and performance as the company expands its agentic capabilities. According to TechCrunch’s October 8 report, Pichai said Gemini had more than one billion monthly active users, while nearly 90% of Fortune 100 companies were using Gemini Enterprise at work.

The existing enterprise customer base gives Google a potential distribution advantage, but business adoption will depend on whether the agent can operate reliably within complex corporate environments.

Security and administration controls

Google says the new system incorporates identity and policy management, authorisation controls, secure sandboxing and network gateways.

These measures are intended to help companies control what agents can access and which actions they can perform. Nevertheless, businesses will need to test the system against their own security requirements, especially when connecting sensitive data or systems that support financial and operational decisions.

Google Introduces New AI Cost Controls

Running AI agents can involve more than the cost of generating individual responses. Agents may execute multiple steps, call external tools, retrieve data and use different models during a single task.

Google is therefore introducing flexible spending options designed to help businesses manage enterprise AI expenses.

The announced controls include multi-model orchestration, smart routing and real-time spending limits. These features are intended to help organisations direct tasks to appropriate models and manage costs as AI usage expands.

For businesses, the economics will depend on how frequently agents are used, the complexity of assigned work, the models selected and the amount of data processed. Automation can save employee time, but it does not automatically guarantee lower operating costs.

Companies will need to compare the cost of running an agent with the time saved, the quality of its results and the additional oversight required.

How Google’s Move Changes the AI Competition

The launch puts Google more directly into competition with AI companies building agents that can complete tasks rather than merely respond to prompts.

OpenAI and Anthropic have been expanding their offerings for coding, research and workplace productivity, while other companies are developing agents for messaging, shopping, customer service and business operations.

Google’s advantage is its existing ecosystem of productivity applications, cloud infrastructure and enterprise integrations. Its ability to connect Gemini with tools employees already use could make adoption easier for organisations that rely on Google Workspace or Google Cloud.

The inclusion of third-party models is also strategically significant. Rather than requiring every task to run on a Google model, the company is positioning its agent as a coordinating layer that can choose among different models.

However, Google will need to demonstrate that this flexibility produces consistent results. Connecting multiple systems introduces challenges involving permissions, data quality, model reliability and the handling of errors across long workflows.

The Bigger Picture

Google’s new Gemini agent reflects the industry’s shift from conversational AI toward systems that can plan and execute multi-step tasks. By connecting workplace applications, databases, specialised tools and different AI models, Google is attempting to make AI agents part of everyday business operations.

The opportunity is significant, particularly for organisations seeking to automate repetitive administrative work, accelerate analysis and improve collaboration. But successful deployment will require more than capable models. Businesses must establish clear permissions, monitor agent actions, measure productivity gains and ensure that humans remain involved in important decisions.

Looking Ahead

The immediate focus will be on enterprise adoption and the performance of Gemini in real-world workflows. Early customers and testers will help determine whether the agent can reliably complete complex tasks across different applications while respecting corporate security policies. The availability of third-party models and new cost controls may make the system more attractive to organisations that want flexibility without losing administrative oversight.

Over the longer term, Google could expand agentic capabilities to more users and additional business functions as the technology matures. The competitive advantage will depend on how effectively Gemini connects to existing systems, handles exceptions and delivers measurable results at a manageable cost. For now, the launch signals Google’s ambition to turn Gemini from an AI assistant that answers questions into a workplace agent that can carry out assigned work.