Build an agent with ADK and Agents CLI in Agent Platform

This document demonstrates how to build, evaluate, and deploy a prototype AI agent using the Agent Development Kit (ADK) with the Agents CLI in Agent Platform. The ADK is an open-source, code-first framework for building sophisticated AI agents. Agents CLI provides a unified, machine-readable interface for AI development tools to interact with the ADK, enabling end-to-end agent lifecycle management. It encapsulates expert knowledge of ADK, agent evaluation, and deployment to Google Cloud, enabling AI development tools to perform complex actions from natural language prompts.

By using Agents CLI with your preferred AI-powered development tool, such as Gemini CLI, Claude Code, or Codex, you can use natural language prompts to define, test locally, and deploy a prototype agent to a Google Cloud runtime.

This tutorial guides you through creating a "caveman compressor" agent, which transforms verbose text into concise summaries, inspired by caveman .

Before you begin

Complete the following prerequisites:

Ensure you have a Google Cloud project and have enabled the Agent Platform API. If not, complete one of the following quickstarts:
- Google Cloud console quickstart for Agent Studio for Gemini Enterprise Agent Platform
- API quickstart for Models
Install an AI-powered development tool, such as Antigravity , Gemini CLI , Claude Code, or Codex. This tool is required to interact with Agents CLI.

Setup

Install uv , a Python package installer. For instructions, see the uv installation guide .
Run the Agents CLI setup command using uvx (included with uv ). This is the only Agents CLI command you execute directly:
```
 uvx  
google-agents-cli  
setup 
```
Open your AI development tool, such as Antigravity.

Scaffold project

Instruct your AI development tool with the following prompt:

 Use agents-cli to build an agent that compresses verbose text into concise, caveman-style summaries.

Your AI development tool activates the google-agents-cli-workflow and google-agents-cli-scaffold skills. It performs the following actions:

Asks clarifying questions, such as its deployment target and safety constraints.
Writes a DESIGN_SPEC.md file capturing the agent's purpose.

Scaffolds the project:

 agents-cli  
create  
caveman-agent  
--prototype  
--yes cd 
  
caveman-agent && 
agents-cli  
install

This process creates a project with boilerplate agent code, tests, and evaluation sets.

Build agent

Your AI development tool edits app/agent.py , replacing the default agent with the caveman compressor logic. It utilizes the google-agents-cli-adk-code skill for ADK design patterns.

The resulting agent definition resembles the following:

  root_agent 
 = 
 Agent 
 ( 
 name 
 = 
 "caveman_agent" 
 , 
 model 
 = 
 Gemini 
 ( 
 model 
 = 
 "gemini-flash-latest" 
 ), 
 instruction 
 = 
 """You are a text compressor. Convert verbose input text into short, simple summaries with a caveman-like tone. Rules: 
 - Omit articles, filler words, and politeness. 
 - Use short sentences and simple words. 
 - Preserve technical terms. 
 - The tone should be abrupt and funny, but the core meaning must be retained. 
 Example input:  "I would like to deploy the application to production environment." 
 Example output: "Me deploy. Production. Now." 
 """ 
 , 
 )

Your AI development tool then performs a basic functionality test:

 agents-cli  
run  
 "Please help me understand the deployment options available for my project"

This is the expected output from that test:

“Deploy options: Agent Runtime, Cloud Run, GKE. Pick one. Ship.”

Evaluate agent

To evaluate the agent, instruct your AI development tool with the following prompt:

 Write evaluations for the caveman agent and run them.

Your AI development tool activates the google-agents-cli-eval skill and performs these tasks:

Creates tests/eval/evalsets/caveman.evalset.json with test cases covering compression quality, technical term preservation, and tone.
Configures LLM-as-judge criteria in tests/eval/eval_config.json .
Runs the evaluation:
```
 agents-cli  
 eval 
  
run 
```

If test cases fail, provide corrective feedback to your AI development tool. For example:

The response to the greeting test is too polite. Make it more brusque.

Your AI development tool adjusts the agent's instructions, re-runs agents-cli eval run , and iterates until the desired quality is achieved.

Deploy agent

To deploy and run the agent on Google Cloud, instruct your AI development tool as follows:

 Deploy this agent to Cloud Run.

Your AI development tool activates the google-agents-cli-deploy skill and:

Adds the necessary deployment infrastructure configuration:

 agents-cli  
scaffold  
enhance  
--deployment-target  
cloud_run

Deploys the agent:
```
 agents-cli  
deploy 
```

The agent is now deployed on Cloud Run. The output includes the service URL, which you use to access the agent.

Observe agent

Cloud Trace is enabled by default. To view traces, open the Cloud Trace explorer in the Google Cloud console and send requests to your deployed agent. You will see spans for LLM calls and tool executions.

To enable more detailed observability, instruct your AI development tool:

 Set up observability infrastructure for my agent.

Your AI development tool provisions a service account, a Cloud Storage bucket, and a BigQuery dataset, and updates the deployed service to use these resources. Refer to the Observability Guide for details.

Summary of actions

This table summarizes the prompts and the corresponding actions performed by your AI development tool:

User instruction	Your AI development tool actions
"Build a caveman compressor agent"	Scaffolds project, writes agent code, tests locally.
"Write evals and run them"	Creates evalset, configures LLM-as-judge, runs `agents-cli eval run` .
"Deploy this to Cloud Run"	Adds deployment target configuration, deploys to Cloud Run.
"Set up observability"	Provisions service account, Cloud Storage bucket, and BigQuery dataset.

Agents CLI skills provide the necessary context for your AI development tool to use appropriate ADK patterns, structure evaluations, and configure deployments.

What's next

Explore more complex agent designs with these prompts:

Add tools:"Integrate a Google Search tool so the agent can access current information."
Multi-agent systems:"Create an agent that can interact with other agents using the adk_a2a template."
RAG:"Build an agent that answers questions based on our documentation using the agentic_rag template."

Learn more about Agent Platform:

ADK : Create, deploy, and orchestrate agentic architectures.
Agent Runtime : Create, deploy, and manage agents.
Pricing : Learn about pricing for Agent Platform.
Agents CLI Quickstart

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