---
title: "Google Cloud Introduces Agents CLI to Streamline AI Agent Development Lifecycle"
source_name: "InfoQ"
original_url: "https://www.infoq.com/news/2026/04/agents-cli-google-cloud/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global"
canonical_url: "https://www.traeai.com/articles/029f58a1-4b67-47a1-8b31-efc102481f39"
content_type: "article"
language: "英文"
score: 7.5
tags: ["Google Cloud","AI","CLI","开发工具"]
published_at: "2026-04-28T17:55:00+00:00"
created_at: "2026-04-28T23:17:06.16696+00:00"
---

# Google Cloud Introduces Agents CLI to Streamline AI Agent Development Lifecycle

Canonical URL: https://www.traeai.com/articles/029f58a1-4b67-47a1-8b31-efc102481f39
Original source: https://www.infoq.com/news/2026/04/agents-cli-google-cloud/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global

## Summary

Google Cloud推出Agents CLI工具，优化AI代理开发的全生命周期管理。

## Key Takeaways

- Agents CLI简化了AI代理开发中的调试、测试和部署流程。
- 新工具支持开发者快速迭代AI模型并提升开发效率。
- CLI工具集成在Google Cloud生态系统中，便于现有用户采用。

## Content

Title: Google Cloud Introduces Agents CLI to Streamline AI Agent Development Lifecycle

URL Source: http://www.infoq.com/news/2026/04/agents-cli-google-cloud/

Published Time: 2026-04-28T17:55:00+0000

Markdown Content:
# Google Cloud Introduces Agents CLI to Streamline AI Agent Development Lifecycle - InfoQ

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# Google Cloud Introduces Agents CLI to Streamline AI Agent Development Lifecycle

Apr 28, 2026 2 min read

by

*   [](http://www.infoq.com/profile/Robert-Krzaczy%C5%84ski/)[Robert Krzaczyński](http://www.infoq.com/profile/Robert-Krzaczy%C5%84ski/)

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Google Cloud has introduced [Agents CLI](https://developers.googleblog.com/agents-cli-in-agent-platform-create-to-production-in-one-cli/) within its Agent Platform, aiming to streamline the development lifecycle of AI agents from local prototyping to production deployment. The release targets a common challenge in agent development, where tooling and infrastructure are often fragmented across multiple services and environments.

The tool is designed to integrate directly with coding agents such as Gemini CLI, Claude Code, and Cursor, providing a unified interface to Google Cloud services, including Agent Platform, Cloud Run, and infrastructure automation components. The goal is to reduce the fragmentation developers face when moving from experimentation to production systems.

Agents CLI provides a programmatic layer that allows coding agents to access predefined "skills" and API references. This enables developers to quickly set up projects with minimal prompts and less manual configuration. Instead of needing extensive context or documentation, developers or their AI assistants can easily initialize projects, define workflows, and configure deployments using a series of CLI commands.

A key focus of the release is reducing context overhead. In many existing setups, coding agents must infer how cloud services connect, often leading to inefficient iterations and increased token usage. By embedding structured knowledge directly into the CLI, Google Cloud aims to make these interactions more deterministic and efficient.

The tool features built-in support for local simulation and evaluation. Developers can run evaluation pipelines, compare outputs from different runs, and validate agent behavior against datasets before deployment. This highlights the increasing focus on testing and reliability in agent-based systems, where accuracy and consistency are essential.

For deployment, Agents CLI automates infrastructure provisioning and release workflows. It can generate Infrastructure as Code (IaC), configure CI/CD pipelines, and deploy agents to managed environments such as Cloud Run or Kubernetes. The CLI also supports publishing agents to enterprise environments, including integration with Gemini Enterprise.

Another feature is the introduction of Human Mode, which allows developers to directly execute CLI commands instead of relying entirely on agent-driven automation. This provides a way to inspect and control workflows when needed, addressing concerns about the lack of transparency in fully autonomous systems. Shivam M. [commented](https://www.linkedin.com/feed/update/urn:li:activity:7452911611297599488?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A7452911611297599488%2C7452990711064600577%29&dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287452990711064600577%2Curn%3Ali%3Aactivity%3A7452911611297599488%29) on this aspect:

> Human Mode is a good addition. It gives a way to verify what’s happening instead of treating the agent as a black box.

Initial reactions from the community highlight both the potential efficiency gains and the growing maturity of agent tooling. Hina Arora [noted](https://www.linkedin.com/feed/update/urn:li:activity:7452911611297599488?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A7452911611297599488%2C7452917566026723328%29&dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287452917566026723328%2Curn%3Ali%3Aactivity%3A7452911611297599488%29):

> This is a major leap forward. With Agents CLI, building, deploying, and managing agents just got so much more efficient.

Agents CLI is available through a simple installation command and includes built-in workflows for creating projects, running evaluations, and deploying agents. Google Cloud provides documentation and a GitHub repository to support adoption and further exploration.

## About the Author

[](http://www.infoq.com/profile/Robert-Krzaczy%C5%84ski/)

#### **Robert Krzaczyński**

Robert Krzaczyński is a software engineer with solid experience in developing web applications. Passionate about applying artificial intelligence algorithms in medicine and the broader healthcare sector, he continuously expands his expertise in ML and AI. He holds a BSc Eng degree in Control Engineering and Robotics, as well as an MSc Eng degree in Computer Science.

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Multi-cloud flexibility often fails at the data layer. This session treats portability as an architectural challenge, exploring patterns like decoupled storage and snapshot replication. It also weighs the trade-offs between latency and consistency for your design. 
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Learn how to navigate multi-cloud backup challenges across AWS, Azure, and Google Cloud. By addressing tool fragmentation and siloed data, teams can ensure consistent policies, reduce costs, and maintain seamless recovery across all providers. 
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