Google is expanding its enterprise AI strategy beyond chatbots and individual productivity tools. The new Google Gemini agent is designed to accept an objective, plan the work, use connected tools, and return a completed result across business applications. Instead of repeatedly prompting an AI assistant for individual steps, employees can delegate more complex assignments and let the agent coordinate the process.
The announcement’s deeper significance lies in how Google separates the agent from the model powering it. The Gemini agent can orchestrate work across Google’s Gemini models and Anthropic’s Claude models, routing jobs to the model it determines is best suited to the task. That makes model choice part of the workplace AI infrastructure rather than tying every task to a single model family.
Gemini Becomes the Agent, Not Just the Model
Google says its Gemini agent can orchestrate work across Gemini models and supported Claude models from Anthropic, routing tasks to the model best suited to the job. Google says support for additional leading private and open models is planned for the future. This does not mean Google is replacing Gemini with Claude, or that every task will automatically run on a Claude model. Instead, the agent acts as the coordinating layer, while the underlying model can vary according to the task’s requirements, expected quality, and cost.
The approach could give businesses more flexibility as AI models evolve. Organizations may be able to retain their workflows, connected data, and agent configurations even when another model becomes better suited to a particular task. Google says Smart Routing automatically directs workloads to models based on performance and cost, while separate real-time spend caps can limit project-level AI spending.
AI Work Continues After Employees Log Off
Another major feature is persistent execution. Because the agent runs in the cloud, it can preserve task context across devices and channels and continue long-running work for hours or days after the user closes their laptop. This reduces the need to restart work or repeatedly explain the same objective. Gemini can also create temporary, task-specific sub-agents to divide complex assignments into smaller jobs. These agents can work in parallel or sequentially, with Gemini coordinating their contributions.
This represents a different approach to workplace AI: users delegate an outcome rather than simply request an answer. However, long-running execution does not guarantee that every task will be completed correctly without human review. Google’s Gemini Spark also reflects the company’s broader push towards AI agents that can handle tasks beyond individual chat responses.
AI Coworkers Get Their Own Digital Identities
Google is also introducing coworker agents designed to handle ongoing responsibilities for a team or organizational role. Unlike temporary sub-agents created for a particular task, these agents can maintain a persistent identity across sessions and changing responsibilities. According to Google, coworker agents can receive their own company email addresses, calendars, persistent storage, and presence in the company directory.
Colleagues can interact with them through familiar Workspace channels, including Chat and document comments. These agents are not human employees, and their access is not unlimited. Coworker agents see only the context shared with them, with access governed by the organization’s existing sharing and membership permissions. Their usefulness will depend on how organizations configure responsibilities, access rights, and oversight.
Integrations Bring Gemini Into Everyday Work
The Gemini agent is designed to work across Gmail, Drive, Docs, Slides, Sheets, Chat and Calendar. Google says Gemini can connect with tools including Microsoft Office and Teams, Slack, Confluence, Git, Jira, Salesforce, ServiceNow and enterprise databases. Support for the Model Context Protocol (MCP) allows the agent to connect with compatible tools and systems. This could help businesses bring AI into existing workflows without replacing every application they already use.
Google also describes enterprise safeguards, including agent identities, permission controls, audit trails and secure execution sandboxes. Google says agent traffic passes through Agent Gateway, which acts as an AI network firewall enforcing organizational policies in real time. These controls will be important as agents gain the ability to execute code and interact with business systems. Google’s Gemini Enterprise platform has already focused on bringing AI into organizational workflows.
Google Gemini Agent: What This Means for Enterprise AI
Google’s announcement positions the Gemini agent as more than another AI assistant. Its combination of model selection, persistent execution, multi-agent coordination, and workplace identities points towards an environment where AI systems can take responsibility for multi-step assignments. The competitive question is no longer only which company has the strongest model. It is also which platform can coordinate models, connect to enterprise data, manage costs and enforce security while delivering useful results.
For businesses, the practical value will depend on reliability, integration quality, permission management and the ability to verify an agent’s work. Google’s announcement establishes its direction, but broader adoption and independently measured outcomes will determine how well the approach works in practice.
Conclusion
Google’s new Gemini agent signals a move towards model-flexible enterprise AI, where the agent’s identity and workflow can remain consistent while the underlying model changes according to the task. Its Claude integration is particularly notable because the new enterprise agent shows Google adopting a multi-model approach for this workplace platform instead of restricting every task to Gemini-family models.
Combined with long-running execution and AI coworkers that have their own digital identities, the system could change how businesses delegate knowledge work. The real test will be whether these capabilities deliver dependable results while keeping enterprise data, permissions, and costs under control.
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