What can you use a Google AI Agent for?

The Vertex AI Agent Engine and Google AI Agents are transforming productivity and enterprise automation for AI today. In this article, you’ll learn that Google’s AI agent is intelligent, and it’s also an autonomous software system built on advanced multimodal large language models such as Gemini. Meanwhile, these agents can reason, plan, and take actions across different systems to achieve complex goals. They process multimodal information—including text, images, audio, and video- in addition to retaining both short-term and long-term memory to learn from interactions and improve over time.

This article is for business leaders, developers, and anyone interested in how Google AI agents are shaping the future of productivity and automation. We cover what Google AI agents are, how they work, key use cases, and how to get started. You’ll learn about the underlying technology, practical enterprise and consumer applications, in addition to developer workflows, pricing, and safety considerations. Understanding Google AI agents matters now because they are rapidly changing how organizations and individuals approach complex tasks, automate workflows, and unlock new levels of efficiency.

If you would like additional information on Google’s AI Agents I’ve also written two articles on LinkedIn about them. The first article is specifically about their functionality within AI Search and the second article explains Google’s other AI Agents in more detail.

What Is a Google AI Agent Today?

They are autonomous agents that interpret natural-language instructions and interact with users, enabling them to process high-level commands and goals. Unlike traditional chatbots that handle single-turn Q&A, Google AI agents use Large Language Models (LLMs) to break down goals into multi-step plans and adjust strategies as new information arrives. They can process multimodal information, including text, images, audio, and video, and retain both short-term and long-term memory for immediate tasks and learning from past interactions. It’s important to understand that Agents operate using a recursive loop known as the ReAct (Reason + Action) framework. Visit the Google Agent homepage to explore what’s available now.

The term “Google AI agent” typically refers to three concrete product families:

Product Family	Purpose	Primary Users
Gemini Agent	Multi step tasks in Gemini Apps	Consumers, individuals
Vertex AI Agent Builder	Enterprise agent development	Developers, IT teams
Research agents (SIMA 2)	3D environment reasoning	Researchers
Consider this example: a user asks Gemini Agent to “plan and book a 3-day New York trip.” Instead of returning text suggestions, the agent sequences steps—checking Gmail for travel preferences, querying Google Maps for itineraries, browsing sites to fill forms, and updating Calendar. The agent utilizes LLMs to break down goals into multi-step plans and adjust strategies as new information is received. The agent reasons, acts, and coordinates rather than simply responding.

The great thing about AI Agents is that they retain both short-term and long-term memory for immediate tasks and learning from past interactions. They can process multimodal information, including text, images, audio, and video.

The rest of this article focuses on practical usage in 2025–2026, covering both consumer and enterprise applications.

Benefits of AI Agents

Enterprise Use Cases

The adoption of AI agents across industries is transforming how organizations approach complex tasks and daily operations. By leveraging platforms like Vertex AI Agent Engine, businesses can deploy agents that efficiently complete tasks ranging from data analysis to customer support, freeing up human teams to focus on strategic and creative initiatives.

Therefore, the ability to design custom agents tailored to specific business needs means that automation can be applied precisely where it delivers the most value, whether in finance, healthcare, logistics, or retail. For more information on an AI search strategist focused on geo, AEO, and AI marketing, check out Alan Rambam’s LinkedIn page.

Pre-built Agents

Pre-built agents, such as those available through Gemini Agent, further streamline the process of integrating AI into existing workflows. These conversational agents can interact with customers, process requests, and provide real-time support, all while maintaining consistency and accuracy. The advanced capabilities of Google’s AI ecosystem—such as seamless access to enterprise data and integration with tools like Google Maps—enable agents to perform tasks that require geospatial awareness or up-to-date business information.

Embodied AI

Embodied AI represents the next frontier, allowing agents to interact with physical environments and execute tasks that traditionally required human intervention. This evolution not only boosts productivity but also reduces errors and operational costs. With Vertex AI, organizations can manage, deploy, and scale agents securely, while ensuring their AI solutions remain robust and adaptable as business needs evolve. Ultimately, the ability of AI agents to access, process, and act on complex information makes them a crucial asset for any enterprise looking to stay competitive in a rapidly changing digital landscape.

Google Gemini Agent in Gemini Apps

Today, the Gemini Agent is an experimental feature within Gemini Apps, available on the web, Android, and iOS in select regions as of late 2025. It helps users perform tasks across connected apps without manually switching between tools.

Requirements

To use Gemini Agent, you need:

• Access to a Gemini subscription
• Experimental features enabled in settings
• A compatible Android device or Chrome browser
• Permissions granted for Gmail, Docs, Drive, and other apps

Core Capabilities

Core capabilities include:

• Delegating tasks like drafting follow-up emails based on recent Gmail threads
• Summarizing long Google Docs and creating action plans
• Using screen automation to tap buttons and fill forms on Android
• Conducting research via live web browsing with deep research capabilities
• Tool integration allows Gemini Agent to interact with external APIs, databases, and enterprise applications to complete tasks

You can access Gemini’s live browser control, navigate sites, click UI elements, and fill non-sensitive fields on your behalf. The agent can also interact with a specific web page, treating each page as a distinct unit of web content for navigation and task execution. Users can take over control at any time for sign-ins, payments, or other sensitive actions—keeping info safe while allowing agents to continuously work through workflows. I wrote two additional articles on Google’s AI Agents for my LinkedIn page, building on my recent digital and AI transformation work.

• Part one is about the Search Agent.
• Part two is about all of Google’s AI Agents.
• Also, if you would like additional information on creating your own MCP Server, I have an article on that as well.

What are the Limitations and Safety Considerations for Google’s AI Agents?

• Monthly task caps apply during early development
• User supervision remains crucial for complex interactions
• Restrictions exist around sensitive data entry
• Safety features prevent unintended or potentially harmful actions
• The feature is experimental and evolving
• Possible risks include the need for careful user supervision, the potential for unintended actions by the agent, and privacy concerns when sharing information with websites or connected apps

How do you access Vertex AI Agent Builder on Google Cloud?

Get started with the Vertex AI Agent Builder, which is Google Cloud’s platform for building, scaling, and governing enterprise-grade conversational agents. It integrates tightly with Gemini models and broader Vertex AI services to support safer AI use in business environments. Before building or deploying agents, you must create and configure a Google Cloud project to manage resources, access controls, and billing.

Main Components

Component	Function
Agent Development Kit (ADK)	Python and Java SDKs for building agents
Agent Garden	Pre-built agents and templates to accelerate development
Agent Engine	Serverless runtime for deployment and scaling
Vertex AI Search	RAG pipelines over enterprise content
Vector Search	Semantic retrieval for grounding

Standard Workflow

1. Set up a Google Cloud project and enable Vertex AI Agent Builder
2. Discover samples and templates in Agent Garden
3. Prototype with ADK in under 100 lines of code
4. Attach enterprise data sources and tools
5. Deploy to Agent Engine with observability and policy controls
6. Monitor, test, and iterate

Governance Features

• Centralized agent registry with version control
• Unique identities and permissions per agent
• Audit logs for compliance teams
• Integration with VPCs, IAM, and Apigee for API management

Enterprise Example

A support triage agent queries Vertex AI Search over Confluence, Google Drive, and Jira to understand customer issues. For complex cases, it hands off to ServiceNow or Salesforce via prebuilt connectors—allowing agents to operate within existing workflows while humans manage escalations.

You may Also Read: humanize ai: The Complete Guide to Turning AI Text into Natural Human Writing

Designing Multi-Agent Workflows With Google’s Agent Development Kit (ADK)

ADK is Google’s primary SDK for building sophisticated single- and multi-agent systems on Vertex AI. Most implementations stay under 100 lines of Python or Java, keeping the process accessible for teams developing agents.

The ADK structures agents around four elements:

• Goals: What the agent aims to accomplish
• Tools: Functions and APIs the agent can call
• Memory: Context retained across interactions
• Policies: Rules governing behavior and tool use

Multi-agent workflows enable specialized agents to collaborate. For example:

• A Planner agent decomposes tasks into steps
• A Researcher agent grounds information via Vertex AI Search
• An Executor agent calls tools and performs actions

These agents pass artifacts—plans, documents, API responses—between them in a coordinated process.

Framework compatibility:

You use the ADK to work alongside open-source frameworks such as LangChain, LangGraph, CrewAI, and AG2. Teams can mix ADK-native agents with other agents while using Vertex AI runtime services.

A conceptual code sketch might look like:

invoice_tool = Tool(name=”create_invoice”, func=create_invoice_fn)

Wire agents

planner = Agent(tools=[], goal=”decompose task”) executor = Agent(tools=[invoice_tool], goal=”execute actions”)

Message flow: planner -> executor

This keeps implementations concise while leveraging Gemini’s advanced capabilities for reasoning and multimodality.

Making Agents Interoperate: Agent2Agent (A2A) and Model Context Protocol (MCP)

Using MCP, Google is pushing beyond single-platform agents by standardizing how agents describe their capabilities and communicate with one another. Agent2Agent (A2A) and Model Context Protocol (MCP) serve as key building blocks for this vision.

Agent2Agent (A2A) is a protocol enabling agents built on different frameworks to:

• Publish their capabilities to other agents
• Negotiate tasks and handoffs
• Exchange structured messages (text, forms, audio/video metadata)
• Maintain security across interactions

Over 50 partners—including Box, Deloitte, Salesforce, ServiceNow, and UiPath—have committed to A2A, transforming isolated agents into interactive partners across SaaS tools and cloud providers.

Model Context Protocol (MCP) standardizes how agents interact with tools and data sources. Whether connecting to APIs, databases, or SaaS apps, MCP ensures that the same integrations serve Gemini-based agents, open-source frameworks, and custom runtimes.

Scenario: To start, the Vertex AI agent needs to update a customer record. Via A2A, it invokes a Salesforce copilot, sharing context through MCP-backed tools. The CRM update happens without custom integrations—agents simply communicate through standardized protocols. Connecting Google AI Agents to Data, Tools, and Guardrails

Effective agents must access up-to-date data and tools while operating within enterprise policies. Vertex AI Agent Builder centralizes these connections.

Find Data access patterns:

• Create RAG pipelines using Vertex AI Search over Cloud Storage, Google Drive, BigQuery, Slack, Jira, and on-premise content
• Configure chunking, filters, and ranking for optimal retrieval
• Use Vector Search for semantic matching

Tool use and capabilities:

• The benefit is that ADK and MCP-based tools expose business functions like “create invoice” or “update CRM record.”
• Apigee API Management provides authentication, rate limiting, and observability
• Over 100 prebuilt connectors available as of early 2026

Guardrails for safer operation:

• Configurable content filters
• System instructions defining boundaries
• Policy checks before execution
• Restricted environments for high-risk operations

Teams can describe exactly what agents can and cannot do, ensuring they respect enterprise boundaries while delivering value.

Grounding Google AI Agents in Trusted Knowledge Sources

It’s important to ground AI responses in authoritative data to reduce hallucinations and improve reliability for users and regulators alike.

Here are the grounding options for Gemini-powered agents:

Source Type	Description
Internal RAG	Vertex AI Search over enterprise content
Google Search	Access to 99% of world search data
Google Maps	250M+ places with 100M daily updates
Partner data	Sources like Dun & Bradstreet
Only Google Maps integration enables geospatial reasoning—experimental in the US—allowing agents to reason about routes, regions, and real-time business details.

Practical use cases:

• A logistics planning agent optimizes delivery routes using Maps data
• A travel agent verifies real-time place information before suggesting itineraries
• A research agent combines internal documents with Google Search for comprehensive answers

Both RAG over enterprise data and grounding with public sources can operate in a single workflow, giving agents access to trusted knowledge while maintaining control over sensitive information.

Self-Improvement and Learning in Google AI Agents

A defining feature of Google AI agents, including those built with Vertex AI, is their capacity for self-improvement and adaptive learning. These agents are engineered to execute complex tasks more effectively over time by learning from each interaction and continuously refining their approach. Through experimental features and ongoing research, Google ensures that its AI agents can navigate new challenges, adapt to evolving data, and explore innovative solutions to problems.

The process of self-improvement is driven by agents’ ability to leverage tools and collaborate with interactive partners. By integrating with a wide array of tools and applications, AI agents can expand their capabilities, discover new ways to complete tasks, and enhance their overall performance. This adaptability is crucial for supporting dynamic business environments where requirements and data sources are constantly changing.

However, as AI agents become more autonomous and capable, it is essential to maintain robust controls to support safer use and prevent unintended or potentially harmful actions. The development process Google follows emphasizes careful testing, transparent interactions, and strong governance to ensure that agents operate within defined boundaries and align with human values. By continually improving both their technical capabilities and their alignment with user needs, meanwhile, Google AI agents are poised to become increasingly reliable, versatile, and valuable partners for humans across a wide range of applications.

Deploying, Scaling, and Governing Agents With Agent Engine

If you want to use Vertex AI agents to handle deployment, scaling, state management, and monitoring across regions.

Key characteristics:

• Serverless and auto-scaling: No infrastructure management required
• Session memory: Both short- and long-term memory for continuous interactions
• Observability: Execution traces, reasoning views, tool-call logs, and performance dashboards

Your teams can test behavior using Vertex AI Example Store and evaluation tooling, enabling A/B tests, human feedback loops, and regression tests over real conversations.

Deployment patterns:

• Expose agents via REST or gRPC APIs
• Embed into web and mobile applications
• Make available as internal tools through Gemini Enterprise

This flexibility allows organizations to navigate varying deployment needs while maintaining centralized governance.

Enterprise Security, Compliance, and Governance for Google AI Agents

If you are interested, enterprise adoption depends on strong security guarantees, transparent behavior, and fine-grained control over data flows.

Identity and access controls:

• As part of their identity, Agents operate with unique service accounts
• IAM policies determine data and tool access
• Role-based permissions limit exposure

Data protection mechanisms:

• Regional data residency options
• Encryption at rest and in transit
• VPC-SC perimeters for network isolation
• Customer data not used for shared model training

Content and safety controls:

• Configurable content filters and blocklists
• Output classifiers for regulated industries
• Custom system instructions for specific use cases

Audit and compliance:

Comprehensive tracing and logging support internal and external audits, with trails covering tool calls, data access, and cross-agent communications.

Pricing, Cost Estimation, and Getting Started

Pricing for Google AI agents depends on Gemini token usage, Vertex AI services, and infrastructure. If needed, Google Cloud’s pricing calculator helps estimate per-region costs.

Common cost drivers:

• Volume of user interactions
• Complexity of RAG pipelines
• Number of tools and connectors invoked per request
• Retention period for logs and memory

Cost optimization practices:

• Cache frequent responses
• Use efficient Gemini model variants where possible
• Set guardrails on expensive tool calls
• Regularly prune unused agents and connectors

Getting started:

1. Visit https://gemini.google/overview/agent/ to explore current capabilities
2. Try Gemini Agent in Gemini Apps with experimental features enabled
3. Build a small proof-of-concept on Vertex AI Agent Builder using Agent Garden samples
4. Connect only non-sensitive test data initially
5. Scale once you’ve validated value and established governance

Future Directions: From SIMA 2 to General Embodied Intelligence

SIMA 2, announced in 2024–2025 by Google DeepMind, represents a research agent that plays, reasons, and learns in virtual 3D game worlds. Powered by Gemini models for perception and planning, it explores what embodied AI might look like at scale.

What makes SIMA 2 different:

• Observes game screens multimodally rather than accessing APIs directly
• Follows complex instructions and performs multi-step actions
• Operates in environments like MineDojo and ASKA
• Generalizes concepts—transferring “mining” or “harvesting” between games
• Demonstrates self improvement through feedback and trial-and-error

Connection to practical agents:

Techniques for long-horizon planning, self-improvement, and multimodal understanding are gradually influencing product features in Gemini and Vertex AI. What starts as research in virtual worlds eventually shapes how production agents address real-world problems.

Always ahead of its partners, Google emphasizes responsible development—SIMA 2 remains a limited research preview, with human-centered design, transparent reasoning, and careful safety evaluation before broader deployment.

FAQ

Is “Google AI Agent” the same as Gemini?

No, Gemini is the family of models and apps, while “Google AI agent” refers to systems built on Gemini that can plan and act autonomously. Whereas, Gemini Agent in Gemini Apps and agents created with Vertex AI Agent Builder both use Gemini models, but add memory, tools, data connectors, and governance layers. In addition, users typically encounter agents through the Gemini web app, mobile apps, or custom enterprise applications.

Do Google AI agents train on my company’s or personal data?

For enterprise workloads on Google Cloud, customer data used with Vertex AI and Agent Builder is not used to train shared Google models per Google Cloud’s data governance commitments. Logs and telemetry remain under your organization’s control. Therefore, for consumer Gemini usage, separate privacy controls apply—review current Gemini data usage policies for details.

Can I integrate non-Google tools and SaaS apps into my Google AI agents?

Yes, Vertex AI Agent Builder and ADK are designed for third-party integrations using APIs, MCP tools, and Apigee-managed services. Existing connectors cover Salesforce, ServiceNow, Box, Slack, Jira, and more. In addition, Agent2Agent (A2A) enables deeper interoperability with other vendors’ agents without custom one-off integrations.

How do I keep a Google AI agent from making unsafe or non-compliant decisions?

Define strict system instructions, policy checks, and content filters that shape what an agent can say and do. Use IAM roles to limit access to sensitive data and tools. Sandboxing constrains high-risk operations. Start with read-only capabilities, monitor traces and logs, then gradually enable higher-impact actions once behavior is well understood.

Where should I start if I just want to experiment with Google AI agents?

Individuals can begin with Gemini Agent in Gemini Apps—enable experimental features and try managing email, summarizing documents, or scheduling events. In addition, developers should start with a small proof of concept in Vertex AI Agent Builder using Agent Garden samples with non-sensitive test data. Explore the Google Agent homepage for documentation and demos.

Who wrote this article?

Alan Rambam, who is an AI Search Strategist and Visibility Engineer. He works in Generative and Answer Engine Optimization, Large Language Model and Entity Optimization, GEO, AEO, LLMO, and EO.

Alan has worked in Artificial Intelligence (AI) for 18 years and has been very engaged with Google’s AI Agent implementation. He has led integrated AI, semantic SEO, and the development of performance-driven ecosystems to train AI search engines and accelerate real-world conversions for Ford, the FBI, and Fleishman-Hillard. In addition, he is also the Author of “Survival Guide for Maintaining Visibility with GEO in the AI Search Era”. Alan is an EVP GEO and tactical storyteller writing AI content and strategy for EO4.AI, which is a new AI search and research framework focused on Entities and Entity Optimization to prepare for Agentic AI and Zims.AI, which is an AI news and information site where Alan uses RAG (Retrieval-Augmented Generation) to power discovery to figure out new ways to help brands in our AI-mediated, zero-click world. On Rambam.com, another research site that Alan oversees, he uses the MCP Model Context Protocol to integrate datasets and see how narrowly we can target and how personal we can make experiences on a content site.