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Expanding Choice in Gemini Enterprise Agent Platform: Introducing Grounding with Parallel Web Search- Google Developers Blog Building scalable AI agents with modular prompt transpilation- Google Developers Blog Systems Engineering Playbook: Optimizing Qwen 3.5-397B MoE on Ironwood (TPU7x)- Google Developers Blog Unlocking the Next Era of On-Device AI with Google Tensor and Pixel- Google Developers Blog LiteRT.js, Google's high performance Web AI Inference- Google Developers Blog Bridging the Domain Gap: AI Race Coach built with Antigravity and Gemini- Google Developers Blog We terminated a TPU mid-training and it recovered in seconds: Introduction to elastic training with MaxText- Google Developers Blog ML Development in VS Code with Google Cloud Power: Workbench Extension Now Available- Google Developers Blog Why we built ADK 2.0- Google Developers Blog Build agentic full-stack apps with Genkit- Google Developers Blog Driving the Agent Quality Flywheel from Your Coding Agent- Google Developers Blog Build reliable multi-agent applications with ADK Go 2.0. Discover our new graph-based workflow engine, built-in human-in-the-loop, and dynamic orchestration- Google Developers Blog Measuring What Matters with Jules- Google Developers Blog Build Cross-Language Multi-Agent Team with Google’s Agent Development Kit and A2A- Google Developers Blog How A2A is Building a World of Collaborative Agents- Google Developers Blog A2UI + MCP Apps: Combining the best of declarative and custom agentic UIs- Google Developers Blog Announcing the Agentic Resource Discovery specification- Google Developers Blog Enhance Security and Trust: New Session Metadata in Sign in with Google- Google Developers Blog Unlocking the Power of the TPU Stack: Introducing our new Developer Hub- Google Developers Blog DiffusionGemma: The Developer Guide Introducing the Google Colab CLI Gemma 4 12B: The Developer Guide Bringing Gemma 4 12B to your Laptop: Unlocking Local, Agentic Workflows with Google AI Edge Supercharge your integration workflow with the Google Pay & Wallet Developer MCP server How the community trained Gemma to "Think" with Tunix and TPUs The latest updates to Google Pay Enhancing Android Checkout with Dynamic Callbacks in Google Pay Empowering Service Providers and Hardware Partners with Gemini for Home Announcing ADK for Kotlin and ADK for Android 0.1.0: Building AI Agents on Android and Beyond Blazing fast on-device GenAI with LiteRT-LM One Year of Innovation: Celebrating 100k Members in the Google Cloud x NVIDIA Developer Community All the news from the Google I/O 2026 Developer keynote A Smarter Google AI Edge Gallery: MCP integration, notifications, and session continuity Google Tensor SDK Beta with LiteRT An important update: Transitioning Gemini CLI to Antigravity CLI Accelerating on-device AI: A look at Arm and Google AI Edge optimization Announcing Genkit Middleware: Intercept, extend, and harden your agentic apps Build Long-running AI agents that pause, resume, and never lose context with ADK Supercharging LLM inference on Google TPUs: Achieving 3X speedups with diffusion-style speculative decoding Building with Gemini Embedding 2: Agentic multimodal RAG and beyond Speeding Up AI: Bringing Google Colossus to PyTorch via GCSFS and Rapid Bucket Building real-world on-device AI with LiteRT and NPU
Agents CLI in Agent Platform: create to production in one CLI
Ivan Cheung, Pier Paolo Ippolito, Elia Secchi · 2026-04-22 · via Google Developers Blog

APRIL 22, 2026 Developer Relations Engineering Agent Ecosystems

AI agents are transitioning from experimental scripts to production services. But while models get smarter, the infrastructure required to build, evaluate, and deploy them remains stubbornly fragmented. Developers and their coding assistants often struggle with isolation, wasting time and tokens ingesting massive amounts of documentation just to bridge the local-to-cloud gap.

fragmented_gcp (1)

Today, we are thrilled to introduce Agents CLI in Agent Platform, the unified programmatic backbone for the Agent Development Lifecycle (ADLC) on Google Cloud.

Agents CLI is a specialized tool designed specifically for AI coding agents (like Gemini CLI, Claude Code, and Cursor). It gives your AI assistant a direct, machine-readable line to the full Google Cloud agent stack (including Agent Platform, Cloud Run, and A2A Integration) turning a fragmented ecosystem into a seamless assembly line.

Let’s take a look at how the Agents CLI streamlines the journey from idea to production in hours, not weeks.

Build Agents with Agents

The biggest hurdle in agent development is context overload. When your coding agent has to guess how disparate cloud components fit together, it leads to endless loops and token waste.

With Agents CLI in Agent Platform, you simply run one command (uvx google-agents-cli) to inject bundled skills directly into your coding environment.

agents_cli_skills

This provides coding assistants the exact sensory input and API references they need to scaffold functional, standard-compliant projects immediately.

# Installing the CLI 
uvx google-agents-cli setup

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If you want to, you can run the CLI commands directly yourself. However, you can even use your favorite coding agent to use the CLI commands via Agents CLI Skills.

For example, you could prompt your coding agent: "I want to create a travel expense agent that can help me auto-approve expenses under $50 and require HITL to approve anything over $50, or any expense that might look out of the norm."

# Your coding agent seamlessly scaffolds the project using automatic defaults
agents-cli create finance-agent -y --deployment-target agent_runtime

# Move into the directory
cd finance-agent

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Local Simulation and Rigorous Evaluation

Building the logic is only half the battle, ensuring it behaves correctly is the other. Before going live, developers need to know their agents meet accuracy thresholds.

Agents CLI can run rigorous evaluation harnesses. By using native commands, your coding assistant can orchestrate unit tests, validate data retrieval, and contrast different evaluation runs to guarantee quality.

# Run evaluations against your ground-truth datasets
agents-cli eval run

# Compare the trajectory scoring and metrics of two runs
agents-cli eval compare evals/run_v1.json evals/run_v2.json

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Seamless Deployment to Production

Going from a local prototype to a secure, globally distributed service shouldn't take 70 days. Agents CLI can automate the entire deployment phase. It seamlessly injects Infrastructure as Code (IaC), sets up CI/CD pipelines, and deploys directly to Agent Runtime / Cloud Run/ GKE.

# Provision the production infrastructure
agents-cli infra single-project 

# Ship the agent to Google Cloud
agents-cli deploy

# Register the deployed agent with Gemini Enterprise for distribution
agents-cli publish gemini-enterprise

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Human Intent + Agent Execution

While the Agents CLI is optimized for agent consumption (Agent Mode), we know developers need deterministic control. That's why the CLI fully supports a Human Mode. You can run these commands directly in your terminal or scripts for immediate, deterministic execution: stepping in whenever you want to guide the "hands and eyes" of the AI.

agents_cli_lifecycle

What's Next?

Get started today by downloading Agents CLI in Agent Platform and running uvx google-agents-cli in your terminal. Dive into our Documentation and GitHub repository to see how your coding assistant can build the next generation of production-grade AI.

Don't forget to join our community on Reddit or the Agent Ecosystems Google Group and share what you're building.

The Agentic Internet is here. Let your agents build it.

POSTED IN: AI • Cloud • Announcements • Problem-Solving