Building Agent Systems on Google Cloud: ADK, Agent Engine, and Gemini

Google Cloud now provides the most complete agent development stack available from any cloud provider. The Agent Development Kit (ADK) handles framework-level development in Python and TypeScript. Vertex AI Agent Engine manages production deployment with Sessions and Memory Bank now generally available. Gemini 3 delivers the intelligence layer with reasoning control and stateful tool use. Together, these three components form an integrated system for building, deploying, and operating autonomous AI agents at production scale. According to Gartner, 33 percent of enterprise software will incorporate agentic AI by 2028, up from less than 1 percent in 2024. This article breaks down each component of the Google Cloud agent stack, how they connect, and how they map to the five layers of operating architecture that production agent systems require.

The Google Cloud Agent Stack at a Glance

The Google Cloud agent stack is a vertically integrated set of services where ADK provides the development framework, Agent Engine provides the production runtime, Gemini provides the intelligence, and Vertex AI unifies the platform. Each layer is independently useful but architecturally designed to work together.

This is not a collection of loosely coupled services. ADK agents deploy directly to Agent Engine. Agent Engine natively hosts Gemini models. Gemini accesses tools registered in the Cloud API Registry. The integration is structural, not bolted on. That structural integration is what separates a platform from a fragmented collection of tools.

Agent Development Kit (ADK): The Framework Layer

The Agent Development Kit is the open-source, code-first framework for building AI agents on Google Cloud. ADK provides the abstractions for agent definition, tool integration, memory management, and multi-agent composition without locking developers into a single model provider or deployment target.

ADK now supports both Python and TypeScript/JavaScript, expanding accessibility beyond the Python-centric AI ecosystem. This matters for production teams. Most enterprise backend systems run on TypeScript or Java stacks. Python-only frameworks force organizations to maintain a separate language runtime for their agent layer, adding operational complexity. ADK's TypeScript support means agent code can live in the same codebase, share types with existing services, and deploy through the same CI/CD pipelines.

The framework is multi-model by design. While optimized for Gemini, ADK supports any model that implements the required interface. This is an architectural hedge against model lock-in -- a concern that matters when agent systems are expected to operate for years, not months. ADK also integrates with Hugging Face, GitHub, and other platforms for tool and model access, creating a broad ecosystem around what is fundamentally a Google Cloud product.

Sessions, memory, and state management are built into ADK at the framework level. Agents maintain conversational context through sessions, retain knowledge across interactions through memory, and persist workflow progress through state. This is the data foundation that production agents require. Without it, every agent interaction starts from zero -- no history, no context, no ability to resume interrupted workflows. ADK solves this at the framework layer so developers do not have to build custom state management for every agent.

Vertex AI Agent Engine: The Production Runtime

Vertex AI Agent Engine is the managed runtime that takes agents built with ADK and deploys them as production services with enterprise-grade reliability, observability, and governance. Agent Engine eliminates the undifferentiated infrastructure work that causes most agent projects to fail in production.

Sessions and Memory Bank reached general availability in early 2026, with billing starting February 11, 2026. This GA milestone signals production readiness -- Google is now charging for these services because they are stable enough for enterprise workloads. Sessions provide managed conversational state across agent interactions. Memory Bank provides long-term knowledge persistence that survives session boundaries. Together they give agents the ability to remember context across days, weeks, or months of interactions with users and systems.

Agent Designer, now in preview, introduces a low-code visual designer directly in the Google Cloud console. This is significant for teams where subject matter experts -- not developers -- define the business logic that agents execute. A compliance officer can visually design an agent's decision flow without writing code, then hand it to engineering for production hardening. This bridges the gap between domain expertise and technical implementation that slows most agent development cycles.

Code Execution, also in preview, allows agents to run code in isolated sandboxes during task execution. An agent can generate a Python script, execute it in a secure sandbox, interpret the results, and incorporate those results into its next action -- all without exposing the host environment to arbitrary code execution. This capability is essential for data analysis agents, financial modeling agents, and any agent that needs to compute rather than just reason.

Agent Engine natively supports the Agent-to-Agent (A2A) Protocol, enabling agents deployed on Agent Engine to discover and communicate with agents on other platforms and in other organizations. The Cloud API Registry provides centralized tool governance, ensuring that agents only access approved APIs and that every tool invocation is logged and auditable. This is the governance architecture that enterprises require before deploying agents with real operational authority.

Gemini 3: The Intelligence Layer

Gemini 3 is the model family that provides the reasoning capability for agents built on Google Cloud. The March 2026 releases -- Gemini 3 Pro and Gemini 3 Flash -- introduce three capabilities that fundamentally change what agents can do in production: reasoning control, stateful tool use through Thought Signatures, and computer use.

Reasoning control through the thinking_level parameter lets developers configure how much computational effort a model applies to each request. A simple classification task can run at low thinking level for speed and cost efficiency. A complex multi-step analysis can run at high thinking level for accuracy. This is not a binary fast-versus-smart tradeoff -- it is a continuous dial that lets architects optimize cost and latency per task within a multi-agent system. An orchestrator can route simple tasks to Flash at low thinking level and complex tasks to Pro at high thinking level, optimizing the entire system's cost profile.

Thought Signatures enable stateful tool use, which is Gemini's ability to maintain reasoning continuity across sequential tool calls. Without Thought Signatures, each tool call resets the model's reasoning context. The agent calls a tool, receives results, and must reconstruct its reasoning chain from scratch before calling the next tool. With Thought Signatures, the reasoning state persists across tool calls, producing more coherent multi-step execution and reducing token consumption by eliminating redundant re-reasoning. For workflows requiring five or more sequential tool calls, this can reduce latency by 30 to 40 percent.

Computer use is the capability for agents to interact with graphical user interfaces -- clicking buttons, filling forms, navigating applications. This matters for the 60 percent of enterprise operations that still depend on legacy systems with no API. An agent with computer use can interact with a legacy ERP system through its GUI, extracting data and entering information just as a human operator would, without requiring the legacy system to be rebuilt or wrapped in a custom API. Gemini 3 Pro and Flash both support this capability, with Flash offering a lower-cost option for high-volume GUI automation tasks.

From Prototype to Production: The Agent Starter Pack

The Agent Starter Pack is Google Cloud's collection of production-ready templates and CI/CD pipelines that eliminate the most common gap in agent development: the distance between a working prototype and a production system. According to industry research, over 80 percent of AI projects stall between proof-of-concept and production deployment. The Starter Pack directly addresses this gap.

The templates include ReAct agents for reasoning-and-acting workflows, RAG agents for retrieval-augmented generation, and multi-agent templates for orchestrated systems. Each template ships with CI/CD pipelines, testing frameworks, monitoring configuration, and deployment scripts. This is not sample code -- it is production scaffolding that teams customize for their specific workflows.

Deployment follows a staged model: sandbox for development and testing, canary for limited production traffic, and full production rollout. This staged approach is standard practice in software engineering but rarely applied to AI agent deployment. Most organizations deploy agents directly from a developer environment to production, skipping the canary phase entirely. The result is predictable -- production failures that could have been caught with 5 percent of traffic instead damage 100 percent. The Starter Pack encodes the staged deployment pattern into its CI/CD pipelines so teams follow production best practices by default.

Google also offers the GEAR Program -- Gemini Enterprise Agent Ready -- which provides skills training and free credits for teams building production agent systems. This reduces the cost barrier for organizations evaluating the platform and accelerates the learning curve for engineering teams new to agent development.

How This Maps to Operational Architecture

Every component of the Google Cloud agent stack maps directly to a layer in the Hendricks five-layer operating architecture: Data Foundation, Process Orchestration, Intelligence Layer, Integration Fabric, and Performance Interface. Understanding this mapping is critical because the technology stack is not the architecture -- it is an implementation of the architecture.

This mapping is not academic. It is how Hendricks designs agent system implementations. The Data Foundation must be in place before agents can maintain meaningful state. The Process Orchestration layer defines how agents coordinate, which determines whether you use ADK's supervisor pattern, handoff pattern, or subagent composition. The Intelligence Layer is not just "pick a model" -- it is configuring reasoning levels per task to optimize cost and accuracy across the entire system.

Organizations that skip the architecture and jump straight to building agents on Google Cloud end up with the same problem they had before: disconnected capabilities that do not compound into operational performance. The operating architecture is what transforms individual agents into a system that delivers measurable business outcomes. The methodology is Diagnose, Architect, Install, Operate -- and the Architect phase is where the Google Cloud stack gets mapped to the specific operational requirements of the organization.

Frequently Asked Questions

What is the Google Cloud Agent Development Kit (ADK)?

The Agent Development Kit is Google Cloud's open-source framework for building AI agents. ADK supports Python and TypeScript, provides built-in sessions, memory, and state management, and works with multiple model providers including Gemini. It is the code-first development layer that handles agent definition, tool integration, and multi-agent composition for production systems.

How does Vertex AI Agent Engine differ from ADK?

ADK is the development framework for building agents. Agent Engine is the managed production runtime for deploying and operating them. ADK handles how you write agents -- the code, the logic, the tool integrations. Agent Engine handles how those agents run in production -- scaling, monitoring, session persistence, memory management, governance, and staged deployment from sandbox through canary to production.

What are Gemini 3 Thought Signatures?

Thought Signatures are a Gemini 3 feature that maintains reasoning continuity across sequential tool calls. Without them, an agent reconstructs its reasoning chain from scratch after each tool invocation. With Thought Signatures, reasoning state persists across tool calls, reducing latency by 30 to 40 percent on multi-step workflows and producing more coherent execution sequences.

What does the Agent Starter Pack include?

The Agent Starter Pack includes production-ready templates for ReAct agents, RAG agents, and multi-agent systems, each with CI/CD pipelines, testing frameworks, and monitoring configuration. It implements staged deployment -- sandbox, canary, production -- so teams follow production best practices by default. It is production scaffolding, not sample code.

How much does Vertex AI Agent Engine cost?

Agent Engine began charging for Sessions, Memory Bank, and Code Execution on February 11, 2026. Pricing is usage-based, scaling with session volume, memory storage, and compute consumption. The GEAR Program provides free credits for teams evaluating the platform. Exact pricing depends on workload characteristics, which is why architectural planning -- including reasoning level optimization -- directly affects cost.

Key Takeaways

Google Cloud's agent stack -- ADK, Agent Engine, and Gemini 3 -- provides the most vertically integrated platform for building production AI agent systems. ADK gives you the development framework with Python and TypeScript support. Agent Engine gives you managed production operations with Sessions, Memory Bank, and A2A Protocol support. Gemini 3 gives you configurable intelligence with reasoning control and stateful tool use. The Agent Starter Pack bridges the prototype-to-production gap with CI/CD pipelines and staged deployment.

The Google Cloud agent stack is the most complete implementation platform available today. But a platform is not an architecture. Production agent systems require deliberate design across all five layers -- Data Foundation, Process Orchestration, Intelligence Layer, Integration Fabric, and Performance Interface. The stack is how you build it. The architecture is what you build.

Hendricks designs and deploys autonomous AI agent systems on Google Cloud. If your organization is evaluating Google Cloud's agent stack and needs the operating architecture to make it production-ready, start a conversation about what that architecture looks like for your operations.