Gemini Agent Changes Enterprise AI: Google’s New AI Coworker Can Run Work Across Apps and Data

• Gemini agent brings one enterprise AI agent across apps, data, devices and other agents.
• It can retain identity, memory and context while handling tasks for hours or days.
• Google is expanding Gemini Enterprise with coworker agents, deep data access and model choice.

Google has introduced Gemini agent at Gemini at Work 2026 as a universal AI agent designed to handle enterprise work across business applications, company data and other AI agents. The idea is to move beyond separate assistants and give organisations one agent that can carry context from one task to another.

The Gemini agent is built around persistent work, deep business context and access across a wide range of environments. Google says nearly 90% of Fortune 100 companies use Gemini Enterprise, while nearly 80% of its customers use AI products, showing how quickly enterprise AI adoption is expanding.

Gemini Agent Is Built as One Enterprise AI Layer

Google describes the Gemini agent around six core principles: unified work, omnipresent access, persistent execution, multi-agent orchestration, deep context and model choice. Together, these are intended to let an organisation use one agent across knowledge work, content creation and coding instead of switching between disconnected AI tools.

Gemini Agent Can Work Across Enterprise Apps

The platform is designed to connect with Confluence, Microsoft Office, Teams, Slack, Google Workspace, Git, Jira, Salesforce, ServiceNow, BigQuery, Databricks, Postgres and Snowflake. It can also work with desktop files and MCP servers, while tools and skills registries allow organisations to build reusable capabilities.

Google is also introducing coworker agents that can have identities such as @agents.company.com. These agents can maintain persistent storage and are limited to the context that has been shared with them, giving organisations a way to treat AI agents more like controlled digital coworkers.

Four Types of Memory Keep Work Connected

Gemini agent uses four memory types to keep work connected across interactions: Session memory for the current task, Semantic memory for known information, Procedural memory for how work should be done and Episodic memory for relevant past experiences. This structure is aimed at reducing the need to repeatedly explain the same business context.

Gemini Enterprise Gets Deeper Workspace Integration

Gemini in Workspace spans Gmail, Drive, Docs, Slides, Sheets, Chat and Calendar. Google has outlined three modes: personal assistance, proactive delegation and coworker agents. In the delegation mode, for example, an agent can prepare a project update deck from information contained in an email and related work.

A coworker agent can have its own Workspace account, email, calendar and Drive access. Its work remains limited to information that has been shared with it, which gives organisations a more controlled approach to autonomous work inside Workspace.

Data, Coding and AI Workflows Move Into the Same System

Google is positioning the Gemini agent for technical work as well. The platform can assist with PySpark, notebooks, model training, troubleshooting and fixing systems autonomously. Its data layer includes Knowledge Catalog, Smart Storage and a Borderless Lakehouse approach covering sources such as Amazon S3, Azure Data Lake, Salesforce Data 360, SAP and ServiceNow.

Google says about 90% of enterprise data is unstructured. Bringing business data, tools and AI agents into a common context is therefore a major part of the Gemini at Work 2026 strategy.

Industry Agents Are Coming to Enterprise Teams

Financial services capabilities are in Preview with integrations including FactSet, LSEG and SEC filings, along with more than 50 foundational skills, confidence scores, data lineage and citations. Legal capabilities are also in Preview, with support involving NetDocuments, iManage, Harvey and Onit, including confidential document handling and redaction.

Google says government, healthcare and retail capabilities are coming soon. Companies including CME Group, Deutsche Bank and Cooley are using related capabilities in their work.

Gemini Enterprise Customers Are Already Building Agents

Google highlighted enterprise deployments ranging from financial services and retail to manufacturing. Tata Steel in India deployed more than 300 specialised AI agents in nine months for areas including manufacturing safety, predictive supply chains and customer complaints, and cut complaint turnaround time by 50%. TCS and Marubeni were also named among Asia Pacific customers.

Other examples include BNP Paribas using LLM@CIB for more than 65,000 employees, Bradesco reducing document review from about one hour to five minutes, Orange Spain deploying more than 1,000 custom agents and SOMPO using more than 10,000 agents for 34,000 employees. Ulta Beauty and Wesfarmers were also highlighted for shopping and administrative use cases.

Security, Spending Controls and Model Flexibility

Enterprise controls cover identity, authorisation, permissions, auditing and policy. Google also highlighted Agent Sandbox, Agent Gateway and OAuth, along with Need to Know access limits. For spending, Smart Routing, real-time spending limits, Cloud Billing Console and project-level chargeback are designed to help organisations manage AI usage.

Google said AI pricing per token has fallen 98% since 2024. The wider platform supports the Gemini family alongside models and infrastructure including Argon, Flash, Omni and Gemma. Google also highlighted its TPU 8i, which it says can deliver up to 80% better price-performance than the previous generation.

Google Is Expanding the Agent Ecosystem

Google said more than 100,000 consultants and developers joined Gemini hackathons over the past month. Accenture has formed a Gemini Enterprise Business Group with Google Cloud, while Google is also expanding third-party agents, connectors and tools such as AI Solution Finder.

The broader approach suggests Gemini Enterprise is being developed as an environment where organisations can combine Google models, other AI models, business systems and specialised agents instead of relying on a single model for every job.

What Gemini Agent Means for Enterprise Work

The Gemini agent could make enterprise AI workflows more continuous by carrying identity, memory and permissions across tasks and environments. The key difference is the emphasis on persistent execution and coordination, where an agent can work alongside specialised agents rather than stopping after one prompt.

Google’s examples also show that the focus is moving from individual AI features to larger business processes. The approach still depends on access controls, shared context and organisation-specific policies, but it gives companies a broader framework for building and managing autonomous work.

Conclusion

Gemini agent is Google’s latest enterprise push towards AI that can work continuously across applications, data and specialised agents. With persistent memory, coworker identities, multi-agent orchestration and broad integrations, Gemini Enterprise is being positioned as a central layer for more autonomous business workflows.

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