Google Launches Gemini Work Agent to Perform Tasks Across Business Apps

Google Cloud has launched a new Gemini agent designed to perform work across business applications, marking another step in the technology industry’s shift from AI chatbots toward systems that can carry out multi-step tasks on behalf of users.

The company introduced the agent at its Gemini at Work event on October 8.

Google describes it as a single universal agent for work that can answer questions, create content, analyse information, write and execute code and complete tasks across connected business systems.

Unlike a conventional chatbot that mainly responds inside one conversation window, the new system is designed to move across workplace applications and use company information while completing a task.

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Gemini Will Work Directly Inside Google Workspace

Google says the agent will operate directly inside Gmail, Drive, Docs, Slides, Sheets, Chat and Calendar.

The important change is that Gemini can carry the same memory, skills and organisational controls across those applications.

A user could therefore begin a task in one application and allow the agent to draw information from another system where they already have access.

For example, an employee could ask Gemini to analyse files in Drive, use that information to prepare a document and then draft related communication in Gmail.

Google says the agent plans the work, chooses tools and completes the task rather than simply returning instructions explaining how the user should do it.

Google Wants One Agent Instead of Separate AI Tools

Businesses currently use different AI tools for writing, coding, analytics and search.

Google is trying to collapse those functions into one interface.

The new Gemini agent can choose among different models and skills depending on the task.

Google says it can handle general knowledge work, media creation, coding and business analysis from the same prompt window.

That is part of a wider industry movement toward AI agents that can perform actions instead of merely generating responses.

Reuters reported that Google’s launch comes as major technology companies increasingly compete to build autonomous assistants capable of operating across applications.

AI Competition Is Moving Beyond Chatbots

The first wave of generative AI competition centred on which company could build the strongest chatbot.

The next battle is increasingly about which system can actually perform work.

That means reading documents, using software tools, writing code, managing workflows and completing tasks across several applications.

The distinction is important.

A chatbot may tell an employee how to create a report.

An agent is designed to gather the necessary information, create the report and potentially place the finished output in the correct application.

This greater level of autonomy also creates new security and governance risks.

Gemini Can Connect to Business Data

Google says the agent can connect to information and systems already used by an organisation.

It is designed to understand company context rather than relying only on information contained inside a single prompt.

That could make it useful for tasks such as analysing internal documents, answering questions about company data or generating operational reports.

Google is also adding new data and analytics skills that allow users to ask questions in ordinary language and retrieve business insights without writing complex database queries.

This could allow employees outside traditional data teams to interact directly with operational and analytical information.

Financial and Legal Teams Get Specialised Tools

Google is also introducing industry-specific versions of the agent.

Financial-services and legal teams will receive specialised tools, connectors, knowledge and skills tailored to their work.

That indicates Google is moving beyond general-purpose productivity AI toward systems designed for industries where access controls and regulatory requirements are more sensitive.

A legal agent, for example, may need access to privileged documents while ensuring that information is not exposed to employees without permission.

A financial-services agent may need to interact with data covered by regulatory restrictions.

Those requirements make security controls central to whether companies will trust autonomous AI systems.

Google Is Building Permission Controls Around Agents

Google says enterprise agents will operate under identity and permission controls.

The company is introducing policy management, authorisation checks, secure sandboxing and network gateways to govern what an agent can access and which actions it can perform.

This becomes especially important as agents gain more autonomy.

An AI system that can only generate text presents one type of risk.

An AI system capable of reading company records, executing code or interacting with applications can cause much greater damage if it receives excessive permissions or is manipulated through malicious instructions.

Enterprise adoption will therefore depend heavily on whether organisations can restrict agents to the same data and systems that an authorised employee is allowed to use.

AI Agents Create a New Security Problem

The shift toward workplace agents creates a security challenge that did not exist at the same scale with conventional chatbots.

An agent may be given access to email, calendars, internal databases and cloud applications.

If the agent is tricked into following malicious instructions hidden inside a document or website, it could potentially misuse those permissions.

This type of attack is commonly associated with prompt injection.

A malicious instruction could attempt to convince the AI system to reveal information, modify data or perform an unintended action.

Google says its enterprise platform includes policy controls, secure execution environments and authorisation mechanisms intended to reduce these risks.

But the wider industry is still working out how safely autonomous agents can operate across sensitive enterprise systems.

Google Says AI Adoption Is Already Widespread

Google says nearly 80% of its Cloud customers now use its AI products.

It also says nearly 90% of Fortune 100 companies use Gemini Enterprise.

The company said nearly 500 Google Cloud customers each processed more than one trillion tokens over the past year.

Those figures suggest that enterprise AI is moving beyond limited experiments.

Google is positioning the new agent for companies that already use AI at scale and now want systems capable of carrying out longer workflows.

Small Businesses Are Also a Target

Google is not limiting the product to large corporations.

The company says small and midsized businesses are also rapidly increasing their use of its AI services.

Google Cloud said AI usage among these businesses increased more than fivefold over the past year.

The company argues that agents can allow smaller teams to automate repetitive work that would otherwise require additional employees or specialist software.

That could include customer support, content creation, analysis and administrative tasks.

But cost and governance could still determine how widely smaller businesses adopt such tools.

Google Is Adding Cost Controls

Running autonomous AI agents can be more expensive than ordinary chatbot queries because agents may make repeated model calls, use several tools and continue working over long periods.

Google is therefore building spending controls into the platform.

The company says Gemini can use multi-model orchestration and smart routing to select a model appropriate for each task.

Administrators can also impose real-time spending limits.

Google has additionally introduced pay-as-you-go options for some Gemini Enterprise workloads, allowing businesses to continue running agents after they exceed subscription quotas.

This means businesses will increasingly need to manage AI agents not only as software users but also as a new category of computing expenditure.

Memory Is Becoming Part of Enterprise AI

Google’s agent platform also includes persistent memory capabilities.

Its Memory Bank service can store and retrieve information used by agents across sessions.

That allows an agent to remember relevant context instead of starting every task from scratch.

Memory can make AI systems more useful.

But it also creates another data-governance issue.

Companies need to know what information is being stored, how long it remains available and which agents or employees can access it.

For regulated industries, that could become as important as the accuracy of the AI model itself.

AI Agents Are Becoming the Next Big Tech Battleground

Google is entering an increasingly crowded race.

Reuters noted that other major technology companies are also introducing AI systems designed to operate more autonomously across applications and services.

The competition is no longer simply over who has the most capable underlying model.

Companies are now competing over who controls the workplace interface through which AI performs tasks.

For Google, its advantage is the enormous number of companies already using Workspace and Google Cloud.

That gives Gemini direct access to applications where employees already spend much of their working day.

Productivity Gains Come With New Risks

The promise of workplace agents is straightforward.

Employees could spend less time copying information between applications, searching through files or performing routine administrative work.

But the same autonomy that creates those productivity gains also increases risk.

If an AI agent misunderstands an instruction, accesses incorrect information or takes an unauthorised action, the consequences can be greater than a wrong chatbot answer.

Companies will therefore need strong permission limits, audit logs and human approval for sensitive actions.

The future of enterprise AI may depend as much on those controls as on the intelligence of the models themselves.

What this means for you

AI at work is moving from answering questions to actually completing tasks across email, documents, calendars, code and company data. That could save employees significant time, but businesses will need to control exactly what these agents can access and which actions they are allowed to take.