What Is Gemini Enterprise Agent Platform?
A CIO who deployed Google's Vertex AI in 2024 now faces a structural decision: rebuild on Gemini Enterprise Agent Platform, which went generally available April 22, 2026, or defend a fragmented development environment that Google has officially moved away from. The new platform does not add to Vertex AI. It replaces it — bringing Agent Studio, the Agent Development Kit, multi-day autonomous agents, and access to over 200 foundation models into one consumption-priced system.
Gemini Enterprise Agent Platform is Google's unified enterprise AI agent development, deployment, and governance system, announced at Google Cloud Next 2026. It is purpose-built for agents that execute complex, multi-step work processes — including workflows that run autonomously for days at a time within secure cloud sandboxes. For CIOs in Hong Kong, it represents a direct challenge to both Microsoft Copilot Studio and Anthropic's enterprise offerings as the primary platform for large-scale agentic AI deployment.
Google Cloud reported 63% revenue growth in Q1 2026, exceeding $20 billion for the first time. A significant portion of that acceleration is attributed to enterprise AI infrastructure demand — the market into which Gemini Enterprise Agent Platform is squarely positioned.
How Does Gemini Enterprise Agent Platform Replace Vertex AI?
Vertex AI was Google's previous enterprise AI development environment: a collection of model training, serving, and pipeline tools that required significant machine learning expertise to operate. Gemini Enterprise Agent Platform replaces Vertex AI as the primary brand and architecture for enterprise AI development at Google. The underlying compute infrastructure is the same, but the product layer has been redesigned around agents, not models.
The platform bundles what previously required separate Vertex AI services into a single offering: model access via Model Garden, agent building tools via Agent Studio and ADK, deployment via Agent Runtime, data connectivity via native ecosystem integrations, and governance via Inbox and policy controls. Pricing shifts from ML-centric compute billing to agent-centric consumption billing.
Existing Vertex AI customers can migrate their workloads. Google has maintained API compatibility for core services while introducing new agent-specific primitives. For enterprises that have built on Vertex AI pipelines, the migration path is incremental rather than a full rebuild — but the direction is clear. Google's enterprise AI roadmap runs through Gemini Enterprise Agent Platform, not Vertex AI.
What Is Agent Studio and Who Should Use It?
Agent Studio is Gemini Enterprise Agent Platform's no-code, visual workspace for building, prototyping, and deploying AI agents. It is designed for knowledge workers, business analysts, and operations teams who need to build functional agents without writing code. Within Agent Studio, users can browse available models in Model Garden, construct prompt workflows visually, connect agents to internal data sources, and deploy agents to production — all without developer involvement.
The target user for Agent Studio is an operations manager or department head who has a clear workflow automation need — weekly report generation, procurement approval routing, compliance document review — but no machine learning background. According to Google's Cloud Next 2026 documentation, Agent Studio also includes a marketplace of partner-built agents from Google Cloud Marketplace, giving non-technical teams immediate access to pre-built agent templates for common enterprise workflows.
For Hong Kong enterprises evaluating build-versus-buy for AI agent deployment, Agent Studio narrows the gap between having an idea and running a production agent to days rather than months, for use cases that fit standard templates. The caveat is customisation: highly specific workflows requiring deep system integration still require the Agent Development Kit.
What Is the Agent Development Kit (ADK) and When Do Enterprises Need It?
The Agent Development Kit (ADK) is Gemini Enterprise Agent Platform's open-source framework for engineers who need to build, debug, and deploy custom AI agents at enterprise scale. Unlike Agent Studio, ADK is code-first: it provides APIs and scaffolding for complex agent logic, custom tool integrations, multi-agent orchestration, and deterministic control flows for compliance-sensitive workflows where agent behaviour must follow a precisely specified path.
ADK supports two orchestration modes that matter for enterprise IT. Generative orchestration allows the agent to plan its own sequence of actions based on context — appropriate for knowledge work and research tasks where the optimal path varies by input. Deterministic orchestration enforces a graph-based sequence — appropriate for compliance, approvals, and any workflow where audit trails require a reproducible decision path.
For enterprises building mission-critical agentic systems — trade processing, regulatory filing, multi-system data reconciliation — ADK is the appropriate entry point, not Agent Studio. The distinction is not about capability level but about control requirements. Regulated industries in Hong Kong, including banking and insurance, will typically require ADK-level deterministic controls for any production agent that touches financial or compliance data.
How Much Does Gemini Enterprise Agent Platform Cost?
Gemini Enterprise Agent Platform uses consumption-based pricing rather than flat subscription tiers. Agent Runtime charges $0.0864 per vCPU-hour and $0.0090 per GB-hour of memory for deployed agents. Session and memory storage costs $0.25 per 1,000 events. Vertex AI Search, used for knowledge grounding, is priced at $1.50 to $6.00 per 1,000 queries depending on tier. Foundation model tokens consumed by agents are billed separately at each model's standard rate.
New Google Cloud customers receive up to $300 in free credits applicable to the platform. Vertex AI Express Mode provides a free trial tier supporting up to 10 agent engines for 90 days without requiring billing to be enabled — useful for proof-of-concept evaluation before committing to production infrastructure costs.
The total cost of running production agentic workflows on the platform depends on three variables: agent compute time, number of model token calls per loop, and storage event volume. For planning purposes, Google recommends enterprises model cost at the workflow level rather than the per-agent level, since most production deployments involve multiple cooperating agents with shared memory and orchestration overhead.
What Are Multi-Day Long-Running Agents and Why Do CIOs Need Them?
Multi-day long-running agents are autonomous AI processes that continue executing a complex workflow across hours or days without requiring constant human intervention or a persistent active session. In Gemini Enterprise Agent Platform, these agents operate within secure cloud sandboxes — isolated environments that can execute model-generated code, manage files, and interact with external systems safely, without access to the enterprise's core infrastructure.
The enterprise use cases for multi-day agents are distinct from standard chatbot or query-response AI. A sales prospecting sequence that researches 500 target accounts, drafts personalised outreach, and schedules follow-up across a week is a multi-day agent workflow. A compliance monitoring agent that continuously reviews new regulatory filings, flags relevant changes, and routes them to the appropriate team is another. These are workflows that today require dedicated human staff operating on recurring schedules.
According to Gartner's 2026 CIO and Technology Executive Survey, 64% of technology executives plan to deploy agentic AI across their organisations within the next 12 to 24 months. Multi-day agents represent the upper tier of that adoption curve — the workflows where the return on investment is highest but the governance requirements are most demanding. Gemini Enterprise Agent Platform's Inbox feature provides a unified dashboard for monitoring active long-running agents, adjusting configurations, and receiving real-time alerts, giving IT operations teams the visibility needed to govern these deployments.
How Does Gemini Enterprise Agent Platform Compare to Microsoft Copilot Studio?
Gemini Enterprise Agent Platform and Microsoft Copilot Studio target the same enterprise buyer but from opposite starting points. Copilot Studio's strength is deep integration with Microsoft 365, Teams, SharePoint, and Azure: if your enterprise runs on Microsoft infrastructure, Copilot Studio agents can access and act on Microsoft data without custom connectors. Gemini Enterprise Agent Platform's strength is model variety (200+ models via Model Garden), open-source development tooling (ADK), and Google's data infrastructure (BigQuery, Google Workspace, Google Cloud Storage).
On pricing model, Copilot Studio charges per message (approximately $0.01 per message for standard agents) while Gemini Enterprise Agent Platform charges per compute resource consumed. For high-frequency, lower-complexity agents — FAQ bots, form assistants, simple routing — Copilot Studio's per-message model is often cheaper. For lower-frequency, high-complexity multi-day agentic workflows, Gemini's compute-based model typically offers better economics at scale.
For Hong Kong enterprises evaluating both platforms, the decision often comes down to your primary cloud and productivity stack. Microsoft-centric organisations will find Copilot Studio's connectors reduce integration cost significantly. Google Workspace and Google Cloud organisations will find Gemini Enterprise Agent Platform's native integrations equally compelling. Organisations running hybrid or multi-cloud environments should run a structured proof-of-concept on each platform against their highest-priority agentic use case before committing.
How Should Hong Kong CIOs Evaluate Gemini Enterprise Agent Platform?
CIOs in Hong Kong evaluating Gemini Enterprise Agent Platform should structure their assessment around four questions. First, which existing workflows require agent persistence of more than a single session? Multi-day long-running agents are the platform's strongest differentiator — if none of your priority use cases require extended autonomy, a simpler platform may suffice.
Second, does your team have the engineering capacity to use ADK, or is Agent Studio the realistic entry point? The no-code path is faster to initial deployment but constrained in customisation. The ADK path is more powerful but requires dedicated engineering resources. Answering this question honestly determines whether a pilot will reflect real production capabilities or a constrained demo.
Third, what is your data residency and sovereignty requirement? Google Cloud offers regional deployment in multiple Asia Pacific zones, including options relevant to HKMA data residency guidelines. Confirming which zones are available and whether Agent Runtime can be constrained to those zones should happen before architecture design, not after.
Fourth, how will you govern multi-agent orchestration for compliance? Deterministic orchestration via ADK provides audit-ready decision paths. Generative orchestration is flexible but less auditable. For regulated workflows in banking or insurance, this governance question determines whether the platform is usable for your highest-value use cases or only for lower-risk internal tools. UD has guided multiple Hong Kong enterprises through this evaluation for both Gemini and competing platforms, and the preparation process typically prevents costly architecture rework six months into deployment.
Ready to Evaluate Gemini Enterprise Agent Platform for Your Organisation?
The platform is technically available today. The harder question is whether your organisation's data architecture, governance framework, and engineering capacity are ready to support a production agentic deployment. UD's enterprise AI team will walk you through every step: from platform evaluation and use case prioritisation, to architecture design and pilot deployment. 28 years of enterprise technology experience in Hong Kong, fully applied to the agentic AI decisions that matter most right now.