Oct. 29, 2025 /Mpelembe Media/ — This document provides a comprehensive technical guide for startups and developers focused on building, deploying, and scaling AI agents on the Google Cloud ecosystem. It details the core components of agentic systems, including model selection, the use of tools for action, and various data architectures for agent memory.
A major focus is placed on ensuring factual accuracy through grounding techniques like Retrieval-Augmented Generation (RAG) and the development process using the Agent Development Kit (ADK) for code-first solutions. Finally, the guide addresses production readiness by introducing AgentOps, a framework that leverages the Agent Starter Pack and continuous evaluation to ensure agents are reliable, secure, and scalable in production environments.
Google Cloud’s AI agent building tools, such as the Agent Development Kit (ADK), Vertex AI Agent Engine, and Gemini Enterprise, balance the need for flexibility, robust deployment, and comprehensive governance by offering distinct pathways tailored to different user requirements and maturity levels, all within a unified, scalable ecosystem.
This balance is achieved by contrasting the code-first, high-flexibility approach of ADK with the application-first, high-governance platform of Gemini Enterprise, while leveraging managed infrastructure for scalable deployment.
1. Flexibility and Customization
Flexibility is maximized through the code-first approach using the Agent Development Kit (ADK). This route is intended for developers and technical startups that require a high degree of control over agent behavior.
| Component | How it Ensures Flexibility | Source(s) |
|---|---|---|
| Code-First Control | ADK provides a robust environment to build, manage, evaluate, and deploy agents, allowing developers maximum control over custom agents geared for specific tasks. | |
| Custom Architectures | Developers can define custom orchestration logic, such as the ReAct framework, to implement complex, multi-step workflows. Furthermore, they can create specialized agents like LlmAgents for flexible reasoning or Workflow Agents (Sequential, Parallel, Loop) for predictable, deterministic processes. | |
| Tool Definition | Agents can be extended with defined capabilities (tools) that can be simple Python functions or wrappers for complex operations like API calls, connecting to proprietary APIs, internal services, data sources, or even delegating tasks to other specialized agents. | |
| Open Ecosystem | Google Cloud champions open standards like the Model Context Protocol (MCP) and the Agent2Agent (A2A) protocol. This commitment ensures interoperability, allowing agents built with ADK to easily integrate tools from popular open-source libraries (like LangChain) and communicate with other agents regardless of their origin or architecture, preventing vendor lock-in. |
2. Deployment and Scalability
Deployment is streamlined through containerization and a choice of managed runtime environments, ensuring the transition from prototype to reliable product is fast and scalable.
| Component | How it Ensures Deployment Efficiency and Scale | Source(s) |
|---|---|---|
| Vertex AI Agent Engine | This is the recommended, fully managed, auto-scaling service specifically designed for deploying, managing, and scaling AI agents built with ADK. It abstracts away underlying infrastructure, allowing engineers to focus on agent logic rather than operational overhead. | |
| Containerization | ADK provides the capability to package the agent into a standard, portable container. This flexibility allows deployment to any compatible cloud environment. | |
| Runtime Choices | Startups can select the optimal platform based on need: Cloud Run offers a cost-effective, serverless architecture that pays only for compute when the agent is active, ideal for handling unpredictable traffic spikes; while Google Kubernetes Engine (GKE) offers the most granular control for established platform engineering teams. | |
| Agent Starter Pack | This provides a production-ready reference implementation that accelerates deployment by bootstrapping new projects with necessary infrastructure components, including Infrastructure as Code (Terraform) and pre-configured CI/CD pipelines (Cloud Build). |
3. Governance and Control
Governance addresses the crucial need to manage, secure, and ensure the reliability of agents, particularly given their non-deterministic nature.
| Component | How it Ensures Governance and Control | Source(s) |
|---|---|---|
| Gemini Enterprise Platform | This platform-based approach is ideal for managing multiple agents and scaling their use across an organization. It acts as a single, secure platform to govern and orchestrate agent taskforces, unifying disparate applications and data sources. | |
| No-Code Governance | Gemini Enterprise includes Agent Designer, a no-code custom agent builder that empowers non-technical domain experts to create agents via a prompt-driven interface, ensuring they operate within the governed platform. | |
| AgentOps Methodology | Agent Operations (AgentOps) is an operational methodology that adapts DevOps principles to AI agents, providing a systematic, automated framework for reliability and responsibility in production. | |
| Rigorous Evaluation | Evaluation moves beyond simple testing (“vibe-testing”) to a systematic, automated process. The framework requires trajectory evaluation (inspecting the step-by-step reasoning using ADK’s built-in observability and tracing) and outcome evaluation (verifying semantic correctness, factual accuracy, and grounding). | |
| Security and Auditing | The Agent Starter Pack provisions a secure foundation using Terraform, enforcing the principle of least privilege via specific IAM roles for tools. ADK creates a granular trace of every thought and tool call, and the Starter Pack operationalizes this data by routing it to BigQuery for a durable, secure audit trail necessary for compliance. | |
| Guardrails | ADK allows implementation of application logic to validate inputs (checking for injection attacks) and filter final outputs for harmful content, integrating these guardrails into the CI/CD pipeline for continuous security testing. |
The Agent Development Kit (ADK) is primarily built for developers and technical startups to create custom, code-first AI agents.
It provides a flexible and robust environment for building, managing, evaluating, and deploying these agents.
Here is a breakdown of what ADK primarily builds and enables:
1. Custom, Flexible AI Agents
ADK facilitates the creation of agents that require a high degree of control over their behaviour. These agents can be designed as both conversational and non-conversational agents, capable of handling complex tasks and workflows. The focus is on implementing multi-step orchestration logic to solve complex business problems.
The core agent architectures built using ADK fall into three main categories:
- LlmAgent (LLM-based): This is the most common agent type, referred to as “Agent,” which uses an LLM (like Gemini) for complex reasoning, dynamic decision-making, and natural language understanding. It is designed to execute the ReAct (Reason + Action) loop.
- Workflow Agents (Deterministic): These are orchestrators that deterministically control how other agents execute in predefined patterns, used for structured processes. They include:
- SequentialAgent: Executes sub-agents in a fixed order.
- ParallelAgent: Executes multiple sub-agents simultaneously for performance optimization.
- LoopAgent: Executes sub-agents in a loop, often until a termination condition is met, used for iterative refinement.
- Custom Agent (BaseAgent subclass): Allows developers to create unique requirements and tailored workflows by inheriting from
BaseAgentand writing custom Python logic.
2. Complex, Collaborative AI Systems
ADK is designed to be multi-agent by nature, making it easy to build highly specialized AI solutions that automate complex, multi-step workflows through flexible orchestration (sequential, parallel, or dynamic).
ADK agents are enabled to collaborate by:
- Using an Agent-as-a-tool pattern, where one agent delegates a task to another specialized agent.
- Participating in the open, interoperable ecosystem via standards like the Agent2Agent (A2A) protocol.
3. Integrated and Tool-Augmented Agents
ADK builds agents that are equipped to interact with external data and systems using defined tools. This includes defining custom functions and APIs that allow the agent to connect to proprietary APIs, internal services, data sources, and other agents.
The tools built with ADK can be diverse:
- Custom function tools (e.g.,
FunctionTool,LongRunningFunctionTool) for proprietary logic. - Google Cloud toolsets for rich integrations with services like Vertex AI Search and BigQuery.
- Wrappers that allow for the direct reuse of tools from popular open-source ecosystems like LangChain (
LangchainTool).
The ADK framework simplifies the process of integrating AI into existing tools and workflows, enabling agents to be deployed into every facet of a startup’s operations.
The deployment location for AI agents built using the Agent Development Kit (ADK) depends on the startup’s needs regarding scalability, control, and operational overhead. Because ADK agents are containerized, they can be deployed to any compatible cloud environment.
Google Cloud offers three primary managed deployment targets for ADK agents, each suited for different maturity levels and technical requirements:
1. Vertex AI Agent Engine (Recommended for Startups)
Vertex AI Agent Engine is the recommended deployment target for startups using ADK.
- Managed and Optimized: It is a fully managed, auto-scaling service specifically designed for deploying, managing, and scaling AI agents built with frameworks like ADK.
- Operational Efficiency: It abstracts the underlying infrastructure, providing the easiest and most direct path to a scalable, secure production endpoint. This allows engineers to focus on the core agent logic rather than operational overhead.
- Agentic Features: As a service designed for agentic workloads, it provides specialized features like the Memory Bank, a managed service to generate and retrieve long-term personalized memories based on conversations.
2. Cloud Run (Serverless and Cost-Effective)
Cloud Run is a versatile serverless platform and managed compute platform for running container-based applications.
- Cost Management: It is an excellent choice for a startup experiencing rapid but unpredictable growth for their new AI-powered feature. Because it is a serverless architecture, the startup only pays for compute when the agent is actively processing requests, making it a cost-effective way to handle traffic spikes without over-provisioning infrastructure.
- Integration: It is suitable for integrating the agent into an existing microservices architecture or for use cases requiring custom container configurations.
3. Google Kubernetes Engine (GKE) (High Control)
Google Kubernetes Engine (GKE) is the best choice for organizations with established platform engineering teams or those needing maximum architectural control.
- Granular Control: GKE provides the most granular control over networking, stateful workloads, and specialized hardware like GPUs and TPUs.
- Operational Alignment: A Series B startup with dozens of microservices might host their new internal automation agent on their existing GKE cluster so that the agent adheres to the same established CI/CD processes, security policies, and monitoring dashboards as the rest of their production services.
Other Deployment Options
Since ADK packages the agent into a standard, portable container, deployment is flexible:
- Agents can be deployed to custom infrastructure.
- Firebase Studio, an AI-assisted development workspace for building full-stack applications, can deploy production apps to Cloud Run, Firebase Hosting, or your own custom infrastructure.
The deployment location for AI agents built using the Agent Development Kit (ADK) depends on the startup’s needs regarding scalability, control, and operational overhead. Because ADK agents are containerized, they can be deployed to any compatible cloud environment.
Download the full guide here