The gap between local development and production on Google Cloud is about to disappear
Anyone who works with AI development knows how frustrating that gap between local environment and production on Google Cloud can be.
You build the agent, test it locally, and when it is time to push to production… it turns into a maze of documentation, disconnected components, and hours lost trying to make everything fit together.
And it is not just you — coding assistants themselves, like Gemini CLI, Claude Code, and Cursor, struggle with this too. They burn through tokens and time trying to figure out how to connect each piece of the ecosystem, which leads to infinite loops and unnecessary rework.
This is exactly the problem that Agents CLI in Agent Platform was built to solve. Launched in April 2026 by Google, it works as the programmatic backbone of what is called the Agent Development Lifecycle, or ADLC — covering everything from zero to production in a single command-line tool. In this article, you will learn how the Agents CLI works in practice, why it matters for anyone building agents today, and what changes when the integration between local development and Google Cloud finally stops being a roadblock. 🚀
What is the Agents CLI and why it showed up now
The Agents CLI is a specialized command-line tool created by Google specifically for AI code agents. Its goal is pretty straightforward: eliminate the fragmentation that exists between the moment a developer builds an agent locally and the moment that agent needs to run for real on Google Cloud. Before it came along, this process involved accessing multiple separate services — Agent Platform, Cloud Run, A2A integration — configuring each one manually, dealing with documentation scattered across different sources, and hoping everything would work together in the end. It was an exhausting cycle that ate up valuable time from people who needed to focus on what actually matters: building efficient agents.
The tool covers the complete lifecycle of an agent — from the initial project creation, through rigorous evaluation with reference datasets, all the way to deployment in production on Google Cloud. This means that with the Agents CLI, developers no longer need to switch between different tools for each stage of the process. Everything is centralized in a single point of control, which drastically reduces operational friction and lets the focus shift back to AI development itself, rather than the infrastructure surrounding it.
Another important detail is that the Agents CLI was designed from the start to work well with modern coding assistants. Tools like Gemini CLI, Claude Code, and Cursor depend on clear, machine-readable context to generate useful suggestions. When the ecosystem is fragmented, these assistants get lost, burning tokens trying to ingest massive amounts of documentation just to understand how components connect. With the Agents CLI injecting bundled skills directly into the coding environment, assistants receive exactly the API references and context they need to generate functional, standards-compliant projects right away.
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How the Agents CLI works in practice
In practice, the Agents CLI offers a set of commands that cover each phase of an agent lifecycle. The flow starts with a single command — uvx google-agents-cli setup — which injects the skills directly into your coding environment. From there, both you and your coding assistant can access the entire Google Cloud stack for agents in an organized and standardized way.
Agent creation with intelligent assistance
Project creation is surprisingly simple. You can, for example, ask your coding assistant something like: I want to create a travel expense agent that auto-approves spending under 50 dollars and requires human approval for any amount above that or any expense that looks out of the ordinary. The assistant, using the Agents CLI skills, generates the entire project structure with the necessary configurations to run within the Google Cloud ecosystem. A command like agents-cli create finance-agent -y –deployment-target agent_runtime handles the scaffolding automatically, eliminating that manual setup phase that often results in hard-to-trace errors down the road.
And here is an important point: you do not necessarily need to use a coding assistant to operate the Agents CLI. The tool works perfectly fine when run directly by the developer in the terminal. This is the difference between what Google calls Agent Mode — optimized for consumption by AI assistants — and Human Mode, which allows direct, deterministic execution. You choose how you want to work, and the tool adapts.
Rigorous local evaluation before deploy
Building the agent logic is only half the battle. Making sure it behaves correctly is the other half. Before putting anything in production, developers need to know if their agents meet expected accuracy thresholds. The Agents CLI solves this with native evaluation harnesses that let you run unit tests, validate data retrieval, and compare different runs to ensure quality.
With commands like agents-cli eval run and agents-cli eval compare, you can execute evaluations against ground-truth datasets and compare trajectory scoring metrics between different versions of the agent. This makes the testing cycle much smoother, because developers can iterate quickly and with confidence, without needing to deploy every single change just to find out if something broke.
Automated deployment to production
And when it is time to push to Google Cloud? The original Google article is pretty emphatic on one point: going from a local prototype to a secure, globally distributed service should not take 70 days. The Agents CLI automates the entire deployment phase, injecting Infrastructure as Code, configuring CI/CD pipelines, and deploying directly to Agent Runtime, Cloud Run, or GKE.
The flow is straightforward: agents-cli infra single-project provisions the production infrastructure, agents-cli deploy sends the agent to Google Cloud, and agents-cli publish gemini-enterprise registers the agent in Gemini Enterprise for distribution. No endless configuration panels, no custom scripts that only the original creator understands. Everything stays structured and reproducible.
The real impact on local-to-cloud integration
One of the biggest wins the Agents CLI brings is consistency between environments. Historically, one of the most common problems in AI development is the classic works on my machine scenario — the agent runs perfectly in the local environment but shows unexpected behavior when it hits Google Cloud. This happens because configurations, dependencies, and connections to external services are rarely identical between the two environments. The Agents CLI tackles this problem head-on by standardizing how the agent is configured from the very first moment of creation.
On top of that, the tool makes teamwork significantly easier. When every member of a team uses the same set of commands and follows the same workflow, it becomes much simpler to collaborate, review code, and ensure the deployment process is reproducible. No more awkward moments discovering that only one person knows how to make the deploy work. The knowledge is coded into the tool itself, accessible to everyone in the same way.
From a maintenance perspective, the impact is also significant. Agents in production need updates, adjustments, and sometimes rollbacks when things go wrong. With the Agents CLI, this maintenance cycle becomes much more controlled, because the tool provides visibility into the current state of the agent and allows changes to be executed in a structured way. This reduces the risk of introducing issues during updates and makes the AI development process much more sustainable in the long run. 🛠️
The direct connection to the Google Cloud ecosystem
Integration with Google Cloud services is native and comprehensive. The Agents CLI connects directly with Agent Platform, Cloud Run, GKE, and the A2A protocol for agent-to-agent integration, without requiring complex manual configurations. This is especially valuable for teams that need to scale AI development without proportionally increasing operational complexity.
The concept behind the tool is to transform what used to be a fragmented ecosystem into a continuous assembly line. Instead of each service operating as an isolated island, the Agents CLI acts as the connector that makes everything flow cohesively. For the coding assistant, this means receiving clear sensory inputs and exact API references. For the developer, it means less time configuring and more time building.
This approach reflects a broader trend that Google Cloud has been following: making AI development more accessible and productive without sacrificing robustness. The Agents CLI does not exist in isolation — it is part of a larger set of resources that, together, form a cohesive environment for anyone looking to build production-ready agents.
What changes for anyone building agents today
For those in the day-to-day trenches of AI development, the Agents CLI represents a pretty concrete paradigm shift. Before, building and deploying an agent on Google Cloud required a level of infrastructure knowledge that often exceeded what was needed for developing the agent logic itself. The developer had to be, at the same time, an AI engineer and a DevOps engineer. With this tool, that barrier drops significantly, because infrastructure complexity gets abstracted behind simple, well-documented commands.
This also has direct implications for experimentation speed. In AI development, iterating fast is essential. The shorter the cycle between an idea and a real test, the more learning happens in less time. With the Agents CLI reducing deployment friction, it becomes much easier to test agent variations in a real environment, collect feedback, and adjust before investing more resources. This is not just a matter of convenience — it is a real competitive advantage for teams that need to deliver results consistently.
The fact that the tool supports both agent-driven and human-driven operation modes is also worth noting. In scenarios where deterministic control is necessary — like CI/CD pipelines or critical deploy stages — the developer can take direct control. In exploratory scenarios, the coding assistant can use the CLI skills to drive the process with more autonomy. This kind of flexibility is the type of design decision that makes a real difference in everyday use.
Key features of the Agents CLI at a glance
To make it easier to visualize what the tool offers, here are the main points that make the Agents CLI stand out in the current AI development landscape with a focus on Google Cloud:
- Complete lifecycle coverage: creation, rigorous evaluation, infrastructure provisioning, and deployment to production, all in a single command-line tool.
- Two operation modes: Agent Mode optimized for AI assistants and Human Mode for direct, deterministic execution by the developer.
- Native evaluation: test harnesses with metric comparison between different agent versions, ensuring quality before deploy.
- Native integration: direct connection to Agent Platform, Cloud Run, GKE, and the A2A protocol, without complex manual configurations.
- Coding assistant compatibility: works with Gemini CLI, Claude Code, and Cursor, providing machine-readable context and reducing token waste.
- Automated Infrastructure as Code: IaC injection and CI/CD pipeline configuration as part of the deploy flow.
- Integrated publishing: direct registration in Gemini Enterprise for agent distribution after deployment.
- Workflow standardization: simplifies teamwork and makes the deployment process reproducible by any team member.
The bigger picture: agents moving from experiment to production
The launch of the Agents CLI reflects an important maturation moment in the AI ecosystem. Agents are moving past the experimental scripts phase and entering the production services phase. But while models keep getting smarter, the infrastructure needed to build, evaluate, and deploy these agents remained stubbornly fragmented. This disconnect was the real bottleneck — not the capabilities of the models, but the difficulty of making everything work together reliably and at scale.
With the Agents CLI, Google takes a concrete step toward solving that bottleneck. The promise is to turn what used to take weeks — or, as the original article mentions, up to 70 days — into a process that can be completed in hours. And when you remove infrastructure friction, you free up space for developers and their AI tools to focus on what actually generates value: the logic, behavior, and intelligence of the agent itself.
The Agents CLI is not just another tool in the Google Cloud ecosystem. It solves a real problem that directly impacts the productivity of anyone working with AI development every day. And when a tool solves a real problem efficiently, it does not need a lot of effort to earn its place. It just makes sense. 🎯
