Gemini Managed Agents Explained: Google's Enterprise Agent Platform in 2026

Every major cloud platform is racing to answer the same question: how do you give enterprises a way to deploy autonomous AI agents without sacrificing control, security, or compliance? Google's answer is the Managed Agents API — the developer-facing core of the Gemini Enterprise Agent Platform, formerly known as Vertex AI Agent Platform.

Unlike the consumer-facing Gemini Agent or the no-code Workspace Studio, Managed Agents are purpose-built for teams that need sandboxed runtimes, configurable network policies, centralized governance, and deep integration with enterprise data. This is Google's play for the infrastructure layer of the agentic enterprise — and it's one of the most comprehensive agent stacks any cloud vendor has shipped.

Here's what it is, how it works, and what it means for teams building on or evaluating enterprise agent platforms.

What Are Gemini Managed Agents?

In Google's terminology, a managed agent is an autonomous AI agent that runs inside a Google-hosted, sandboxed Linux environment — provisioned, governed, and orchestrated via the Managed Agents API on Google's Agent Platform.

This is meaningfully different from lighter-weight "agent" patterns you might build by chaining Gemini API calls. A managed agent runs in a dedicated runtime (powered by Google's Antigravity harness), can reason across multi-step workflows, execute code, call tools, access mounted enterprise data, and interact with external services — all within strict, configurable security boundaries that Google manages on your behalf.

The practical upshot: developers define what an agent should be able to do through configuration, and Google handles the infrastructure — no VMs to manage, no containers to orchestrate, no sandbox to build.

The Managed Agents API: Control Plane and Data Plane

The Managed Agents API is split into two surfaces that map to a familiar cloud pattern: control plane and data plane.

Agents API (Control Plane)

The Agents API handles the lifecycle of managed agents — creating them, configuring them, updating them, and governing them at an organizational level. Using this API, developers can:

• Define an agent via configuration (YAML/JSON-style) that specifies its tools, data mounts, environment variables, and outbound network allowlist.
• Mount enterprise data sources — including Workspace data, internal document stores, or external APIs — that the agent can access during execution.
• Apply security and governance policies aligned with Gemini Enterprise's DLP and access control rules.
• Manage agent lifecycle events (create, update, disable, audit) programmatically, making it straightforward to integrate into existing CI/CD or infrastructure tooling.

This separation of provisioning from runtime invocation is intentional: it lets IT and security teams govern what agents can do before any user ever interacts with them.

Interactions API (Data Plane)

The Interactions API is the runtime interface — how applications actually talk to a running managed agent. Through this API, callers can:

• Send prompts and tasks to a specific agent instance.
• Receive streaming or batched responses as the agent reasons and executes tool calls.
• Observe intermediate steps — planning, tool invocations, code runs — depending on logging configuration.

The control/data plane split means a single agent definition can serve many callers without each caller needing to understand the agent's internals. It also makes it easier to swap out agent configurations at the control plane without disrupting downstream applications.

The Antigravity Sandbox Runtime

The execution environment for managed agents is Google's Antigravity harness — a Linux sandbox provisioned per agent that provides a secure, isolated environment for multi-step agentic work.

Inside this sandbox, a managed agent can:

• Reason and plan using Gemini models (including Gemini 3.5 Flash, tuned for longer agentic workflows, and Gemini 3.1 Pro for more complex tasks).
• Call tools — web search, code execution, function calling, and custom tools defined via external connectors.
• Execute code, read and write files, and complete multi-step workflows while adhering to the network constraints defined in the agent's configuration.

Google manages the sandbox entirely. Developers configure behavior through the Agents API; the platform handles isolation, resource limits, and security boundaries. This is the core infrastructure bet: that enterprises would rather configure governance policies than operate sandboxed runtimes themselves.

The Broader Gemini Enterprise Agent Platform

The Managed Agents API is one layer of a larger stack Google calls the Gemini Enterprise Agent Platform. Understanding where managed agents sit within that stack matters for anyone evaluating or building on it.

Each layer feeds upward into Gemini Enterprise, which provides a centralized view of every agent in an organization — Google-built, employee-built, custom ADK agents, and third-party partner agents — all subject to the same visibility and policy controls.

Workspace Studio Agents vs. Managed Agents: What's the Difference?

One source of confusion in Google's agent narrative is the relationship between Workspace Studio agents and Managed Agents API agents. They solve different problems at different levels of the stack.

Workspace Studio is the no-code/low-code surface where everyday business users design, manage, and share AI agents ("flows") to automate work across Gmail, Drive, Chat, Sheets, and connected third-party apps like Asana, Jira, Slack, Salesforce, and Mailchimp. These agents are designed to be built in minutes using natural-language prompts, pre-built steps, templates, webhooks, and Apps Script steps — no engineering required.

Managed Agents target developers who need deeper control: sandboxed runtimes, configurable network allowlists, custom tool definitions, data mounts, and enterprise security policies. They're the right choice for complex back-office workflows, multi-system automation, and scenarios where governance and auditability are non-negotiable.

Over time, Google is connecting these layers. Workspace Studio agents can invoke custom agents built on the Agent Platform, and both surface through Gemini Enterprise's governance console. The stack is designed to be composable — different teams building at the level that matches their skills and requirements.

Google's Built-In Agents: The Reference Implementations

Alongside the developer-facing APIs, Google has shipped several first-party agents that demonstrate what the platform can do — and serve as reference implementations for the kinds of workflows managed agents are suited for.

Deep Research performs hundreds of web and enterprise searches, plans a research strategy, and synthesizes results into a structured report. What might take a team weeks to compile manually can be turned around in hours.

NotebookLM Enterprise is an AI-powered research and writing agent that summarizes, extracts, and answers questions across dense document sources — grounded in your organization's own content and governed through Gemini Enterprise.

Gemini Code Assist and CodeMender target developer productivity and security. CodeMender specifically identifies vulnerabilities in codebases, suggests and tests fixes, and applies patches with developer approval — a closed-loop security workflow.

Gemini Spark (covered separately in our Gemini Spark deep dive) is a persistent personal AI agent in Gemini Enterprise that carries out multi-step tasks across Workspace and custom connectors, runs recurring workflows, learns new skills, and requests approval before high-risk actions like sending emails.

Gemini Agent in Gemini Apps is the consumer-facing version — a Labs feature that lets users delegate multi-step tasks like email categorization, drafting replies, calendar restructuring, and web research. It requires a Google AI Ultra subscription and is currently limited to certain regions.

Together, these built-in agents show Google's approach: dogfood the platform publicly, demonstrate the use cases, and let enterprise teams replicate or extend the patterns.

Security, Governance, and Compliance

Centralized governance is the core differentiator Google is betting on with this platform. For IT and security teams, the controls include:

Central visibility. The Gemini Enterprise app provides a single console where administrators can see every agent in the organization — Google-built, employee-built, custom ADK agents, and third-party partner agents — along with their configurations, access levels, and status.

Sandboxed execution. Every managed agent runs in a Google-hosted Linux sandbox with a configurable network allowlist. Agents can only reach the external services explicitly permitted in their configuration, reducing the risk of unauthorized data access or lateral movement.

Policy enforcement via Agent Gateway. Traffic from agents to external data sources and services passes through an Agent Gateway that enforces DLP (Data Loss Prevention) and security policies. Agents authenticate to external tools with scoped credentials — not broad API keys.

Auditability. Administrators can inspect and audit agent activity logs, supporting compliance requirements that demand a full record of what agents did, when, and why.

Consumer-facing Gemini Agent adds an additional layer of user-facing guardrails: explicit safety guidance (including a recommendation not to enter passwords in chat), and a caution against scheduling high-stakes recurring actions given the possibility of model errors.

Developer Experience and Patterns

For teams integrating the Managed Agents API into existing infrastructure, the experience is configuration-driven and REST-first. Common implementation patterns include:

Defining agents declaratively through configuration files that specify tools, skills, data connections, and runtime constraints — then committing those configurations to version control like any other infrastructure-as-code artifact.

Using the Agents API to create and update agents programmatically as part of CI/CD pipelines, so agent deployments follow the same review and approval process as application deployments.

Invoking agents via the Interactions API from back-end services, orchestration layers, or other agents — with Agent-to-Agent (A2A) protocols enabling managed agents to call each other across systems.

Antigravity 2.0, available as a standalone desktop app and CLI, gives builders additional tools for development-time steering, customization, and local orchestration testing before deploying to the managed platform.

Use Cases Where Managed Agents Excel

The platform is optimized for complex, multi-step workflows that benefit from sandboxed execution and enterprise data grounding. Documented use cases include:

Cross-system automation — orchestrating workflows across Workspace, Jira, Salesforce, and internal systems using connectors and custom tools, without building custom integration middleware for each pair of systems.

Research and analysis — multi-step market research, competitive analysis, and due diligence workflows using Deep Research-style agents grounded in both web content and internal documents.

Knowledge management — summarization, Q&A, and insight extraction across dense enterprise document stores, powered by NotebookLM-style agents with Workspace and Drive integration.

Developer productivity and code security — automated code review, refactoring, and security patching via Code Assist and CodeMender, with human approval gates at each critical step.

Recurring workflow automation — using Workspace Studio for everyday automations (email digests, pre-meeting briefs, support triage) while using Managed Agents for more complex orchestration that requires custom tooling or strict governance.

The Ecosystem: Marketplace, Partners, and ADK

Beyond the core APIs, Google has built an Agent Marketplace where organizations can discover, evaluate, and deploy partner-built agents. The marketplace is filterable by industry, use case, and validation status (including Gemini Enterprise compatibility certification).

Third-party integrations span the common enterprise tool landscape: Asana, Jira, Mailchimp, Salesforce, Slack, Teams, and others — accessible via connectors, webhooks, and custom steps in Workspace Studio.

The Agent Development Kit (ADK) is the pro-code path for teams that want maximum control. ADK agents are fully custom implementations hosted on Vertex AI Agent Engine but surfaced and governed through Gemini Enterprise alongside managed agents. ADK and managed agents are complementary, not competing: managed agents handle the common case of config-driven autonomous workflows; ADK handles the cases that require bespoke orchestration logic.

Trade-offs and Considerations for Builders

For teams building agentic products or evaluating enterprise agent platforms, Gemini managed agents offer a compelling combination of managed infrastructure, governance tooling, and Workspace integration. But the trade-offs deserve honest consideration.

Lock-in is real. The Antigravity runtime, the Agents API, and the Agent Gateway are Google-proprietary. Agents built on this stack are meaningfully coupled to Google Cloud. Teams that need to run agents on-premises, in a hybrid cloud, or across multiple cloud providers will find the platform constraining.

Data residency and compliance constraints depend on Google Cloud's regional offerings, which may not satisfy every regulatory regime. Teams with strict data sovereignty requirements should verify regional availability before committing.

Model flexibility is limited. Managed agents run on Gemini models. Organizations that want to route specific workloads to other frontier models — or that want to run open-source models for cost or privacy reasons — need additional architecture to achieve that flexibility.

Governance is a feature, not a constraint, for the right organizations. Enterprise IT and security teams often see centralized governance as a prerequisite, not a nice-to-have. For those organizations, Gemini Enterprise's visibility and policy controls can accelerate internal approval for agent deployments.

For teams building independent, open-source AI coworkers or multi-model orchestration platforms, the practical path is often integration: treating Gemini managed agents as one deployment target while keeping core orchestration logic cloud-agnostic.

Where Gemini Managed Agents Sit in the Competitive Landscape

The clearest advantage for Google is the depth of Workspace integration combined with a no-code-to-pro-code spectrum that lets different teams build at the level that matches their skills. The clearest gap relative to AWS is model breadth — Bedrock's multi-model catalog gives teams more flexibility without leaving the managed infrastructure.

Final Take

Gemini managed agents are the most complete picture Google has painted of what an "agentic enterprise" platform looks like: a sandboxed runtime, a config-driven developer API, a no-code surface for business users, first-party reference agents, centralized governance, and a marketplace for third-party extensions. The pieces fit together coherently.

The real question for enterprise buyers is not whether the platform is capable — it clearly is — but whether the trade-offs of Google Cloud lock-in, Gemini-only model support, and data residency constraints are acceptable for their context. For organizations already heavily invested in Google Workspace and Google Cloud, the answer is likely yes. For organizations that need model flexibility or hybrid-cloud deployment, the platform's managed convenience comes with architectural constraints worth carefully evaluating.

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What This Means for Eigent

Gemini's managed agent architecture — sandboxed runtimes, governed tool access, agent-to-agent coordination — reflects a set of infrastructure bets that Eigent is independently building toward on an open, model-agnostic foundation. On the roadmap: deeper support for isolated agent workspaces that mirror the sandboxing guarantees of managed runtimes, and multi-agent orchestration protocols that work across Gemini, Claude, GPT, and local models — so teams aren't forced to choose a single vendor to get enterprise-grade agent coordination.

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Frequently Asked Questions

What are Gemini managed agents?

Gemini managed agents are autonomous AI agents that run in a Google-hosted, sandboxed Linux environment, provisioned and governed via the Managed Agents API on the Gemini Enterprise Agent Platform. Developers configure the agent — specifying tools, data mounts, and network policies — and Google manages the underlying infrastructure, security, and runtime.

How is the Managed Agents API different from using the Gemini API directly?

Using the Gemini API directly requires you to build your own orchestration, tool-calling logic, sandboxing, and governance. The Managed Agents API provides a fully managed runtime (the Antigravity harness) where all of that infrastructure is handled by Google. The trade-off is less flexibility in exchange for less operational overhead.

What is the Antigravity harness?

Antigravity is Google's agent execution runtime that powers managed agents. It provisions a Linux sandbox per agent where the agent can reason using Gemini models, execute code, call tools, access mounted data sources, and complete multi-step workflows — all within the security constraints defined in the agent's configuration.

How does Workspace Studio differ from the Managed Agents API?

Workspace Studio is a no-code/low-code interface for business users to build and share automation agents across Gmail, Drive, Docs, Sheets, and third-party apps. The Managed Agents API is a developer-facing API for creating more complex agents with sandboxed runtimes, custom tooling, and enterprise security controls. The two are complementary: Workspace Studio for everyday user-built automations, Managed Agents for back-office or security-sensitive workflows.

What is the Agent Development Kit (ADK)?

The Agent Development Kit is Google's pro-code SDK for building fully custom AI agents hosted on Vertex AI Agent Engine. ADK agents offer maximum flexibility for bespoke orchestration logic, but require more development effort than the config-driven Managed Agents API. Both ADK and managed agents are governed through Gemini Enterprise.

Does the Managed Agents API support models other than Gemini?

No. The Managed Agents API is designed for Gemini models (including Gemini 3.5 Flash and Gemini 3.1 Pro). Teams that want to route workloads to other frontier models or open-source models need a model-agnostic orchestration layer outside Google's managed stack.

What governance controls does Gemini Enterprise provide?

Gemini Enterprise provides a centralized console where administrators can view all agents in the organization (Google-built, employee-built, custom, and third-party), manage access policies, enforce DLP rules via the Agent Gateway, and audit agent activity. Managed agents run in sandboxed environments with configurable network allowlists, limiting what external services an agent can reach.

Is the Gemini Enterprise Agent Platform the same as Vertex AI Agent Platform?

Yes. Google rebranded Vertex AI Agent Platform to Gemini Enterprise Agent Platform as part of its broader effort to position Gemini as the brand for its enterprise AI stack. The underlying infrastructure and APIs are the same; the branding reflects Google's consolidation of its AI products under the Gemini umbrella.