How to Build an AI Agent with Gemini CLI and Agent Development Kit
Most businesses struggle to implement AI solutions that actually work in production. The Gemini CLI and Agent Development Kit provide a battle-tested framework for creating intelligent agents that can execute real-world tasks. Learn how to go from concept to deployed agent in this comprehensive guide.
Introduction to Gemini CLI
The Gemini CLI represents a paradigm shift in developer productivity. Traditional coding requires precise syntax knowledge and manual implementation, but with Gemini CLI, developers can focus on intent rather than implementation details. This tool brings the power of Google's Gemini AI models directly into your terminal through natural language prompts.
At its core, the Gemini CLI serves as an extension of developer will - what the community calls "vibe coding." Instead of writing line after line of code, you describe what you want to accomplish in natural language, and the CLI translates that intent into functional applications. This approach dramatically reduces the cognitive load of development while maintaining the precision needed for production systems.
Key Insight: The Gemini CLI isn't just a code generator - it's a complete development environment that understands context, maintains state, and can connect to external tools through MCP (Model Context Protocol) servers.
Vibe Coding Basics
Vibe coding represents a fundamental shift in how developers interact with their tools. The process begins with setting up your development environment in Cloud Shell Editor, which provides the ideal IDE experience for this workflow. Unlike traditional coding, where you need to know exactly what to type, vibe coding allows you to express your intent naturally.
A simple command like "create a personal profile website with a futuristic theme" can generate both the HTML structure and CSS styling automatically. The Gemini CLI calculates the sequence of actions needed - creating files, writing appropriate code, and even starting a local web server to preview the results. This workflow is particularly powerful for rapid prototyping and iterative development.
However, with great power comes the need for discipline. The "yellow flag" mode (which allows automatic execution without confirmation) should be used carefully, especially in production environments. While incredibly useful for labs and development, it's important to maintain version control and implement guardrails as we'll explore later.
Connecting External Tools
Real-world AI agents need to interact with external systems and services. This is where MCP (Model Context Protocol) servers come into play. These specialized portals allow your AI agent to connect with tools like Git repositories, image generation services, and other APIs.
The connection process involves:
- Setting up the external service (like a local Git server)
- Spinning up an MCP server that acts as an intermediary
- Configuring the Gemini CLI's settings.json file to recognize the new arsenal of tools
For example, connecting to a Git server enables version control capabilities directly through natural language commands. You can instruct the agent to "commit these files to the shadowblade-profile repository" without needing to know the exact Git commands. Similarly, connecting to an image generation service like Vert.Ex AI allows for dynamic content creation as part of your workflow.
Implementation Tip: Always verify tool connections by checking the settings.json file and testing basic commands before proceeding with complex workflows.
Context Engineering
As powerful as LLMs are, they can sometimes produce unexpected results. Context engineering provides the guardrails needed to keep your agent's behavior aligned with your goals. This involves providing instructions at three levels:
- System Level: Base instructions and prompts that define the agent's fundamental behavior
- Short-term Memory: Conversation history and design documents that provide immediate context
- Long-term Memory: Policies, style guides, and best practices that persist across sessions
The agentesign.mmd file serves as a blueprint, enforcing clarity on goals and technical implementation before significant development begins. Meanwhile, the gemini.md file in your project's root directory acts as a persistent rulebook, containing Python best practices and coding guidelines that the agent will always reference.
This multi-layered approach to context ensures your agent maintains discipline while still allowing for creative problem-solving within defined boundaries. It's the difference between an uncontrolled AI and a professional developer assistant.
Building the Agent
With our tools connected and context established, we're ready to build our agent's core logic. The Gemini CLI can generate this with a single command that incorporates all our guardrails and connected tools. The resulting agent is designed to use an MCP toolset defined in mcps/server.py.
The agent's capabilities can be expanded by adding new tools to its arsenal. For example, we might add seven new functions decorated with ADK's MCP tool decorator, exposing them to the agent through the MCP server. These could include:
- File management utilities
- Data processing functions
- API integration tools
- Content generation capabilities
It's crucial to review the generated code to ensure it meets your standards and implements the desired functionality correctly. While the Gemini CLI is powerful, human oversight remains essential for production-quality systems.
Testing with ADK
The Agent Development Kit (ADK) provides robust evaluation tools to verify your agent's behavior. ADK eval runs your agent against a golden dataset of challenges supplemented with synthetic data to test various scenarios.
The evaluation process involves:
- Defining test cases in eval_set.json
- Configuring evaluation criteria in test_config.json
- Running the agent through the test gauntlet
- Reviewing results to ensure proper tool usage and responses
For automated CI pipelines, we can use Piest to script these evaluations. This ensures every change is automatically tested before deployment. The test script uses the same agent evaluator library as the ADK eval command, maintaining consistency between manual and automated testing.
Critical Check: Always verify that your agent passes all tests before proceeding to deployment. The evaluation should confirm that the agent chooses the right tools for each scenario.
CI Pipeline Automation
A robust CI/CD pipeline is essential for maintaining quality as your agent evolves. The Gemini CLI can generate a Cloud Build.yaml file that automates the entire process:
- Running the Piest test script to verify agent logic
- Building the agent into a Docker container if tests pass
- Saving the container to Artifact Registry
This automation ensures that only properly tested versions of your agent progress to deployment. The pipeline can be triggered by code commits or run manually when needed. Monitoring the build progress through the Cloud Console provides visibility into each step.
The true power of this approach becomes apparent when making iterative improvements to your agent. Each change is automatically validated before potentially affecting your production environment.
Deployment to Cloud Run
The final step in our journey is deploying our battle-ready agent to Cloud Run. This serverless platform is ideal for AI agents because it:
- Automatically scales to handle varying workloads
- Manages infrastructure so you can focus on your agent
- Provides a secure, public endpoint for your service
Deployment involves taking the container from Artifact Registry and launching it on Cloud Run. Once deployed, your agent becomes a live public service capable of handling real-world tasks. The entire process - from initial concept to deployed agent - can be accomplished remarkably quickly using the Gemini CLI and ADK.
This marks the completion of your mission as a developer in the Agentverse. You've mastered the Gemini CLI, forged your digital identity, crafted an agent with guardrails, evaluated its performance, and automated its deployment - all through the power of vibe coding and Google's AI tools.
Watch the Full Tutorial
For a more detailed walkthrough of building an AI agent with Gemini CLI, watch the full tutorial video. At 8:45, you'll see the actual process of connecting to the Git server, and at 12:30, the image generation workflow is demonstrated in detail.
Key Takeaways
Building AI agents with Gemini CLI and the Agent Development Kit represents a significant leap forward in developer productivity and AI implementation. By mastering these tools, you can create powerful, production-ready agents that would traditionally require extensive development resources.
In summary: The Gemini CLI enables natural language development through vibe coding, MCP servers connect your agent to essential tools, context engineering provides necessary guardrails, and ADK ensures quality through testing - all leading to seamless deployment on Cloud Run.
Frequently Asked Questions
Common questions about building AI agents with Gemini CLI
The Gemini CLI is a powerful tool that brings Google's Gemini AI models directly into your terminal. It allows developers to transform natural language prompts into functional applications through a process called vibe coding.
Unlike traditional coding interfaces, the CLI serves as an intelligent intermediary between your intent and the execution of complex tasks. It maintains context across commands and can connect to external tools through MCP servers.
- Enables natural language development
- Maintains state and context
- Integrates with external tools and services
An MCP (Model Context Protocol) server acts as a specialized portal that allows your AI agent to interact with external tools and services. These servers bridge the gap between your agent's natural language understanding and the concrete actions needed in the real world.
By connecting to MCP servers, your agent gains the ability to perform tangible actions like managing files, querying databases, and generating images without needing to understand the underlying API complexities.
- Acts as an adapter between agents and tools
- Enables concrete actions from natural language
- Can connect to various services simultaneously
Context engineering involves giving instructions to the LLM at different levels to optimize output. This multi-layered approach ensures your agent behaves predictably while still being flexible enough to solve problems creatively.
The three main levels are system instructions (base behavior), short-term memory (conversation history), and long-term memory (policies and style guides). Together, they form a comprehensive framework for agent behavior.
- System level: Foundational prompts and instructions
- Short-term: Conversation history and design docs
- Long-term: Persistent policies and best practices
The ADK includes powerful evaluation tools that run your agent against a comprehensive set of test cases. These evaluations verify that your agent responds correctly to various scenarios and uses the appropriate tools for each situation.
The test framework uses a golden dataset of challenges supplemented with synthetic data to ensure thorough coverage. Configuration files define both the test cases and the criteria for successful completion.
- Tests agent responses against known scenarios
- Verifies proper tool selection and usage
- Can be automated in CI/CD pipelines
Deploying to Cloud Run involves several key steps that ensure your agent is properly packaged and validated before going live. The process begins with successful completion of all tests in your CI pipeline.
First, your agent is built into a Docker container and stored in Artifact Registry. Then, Cloud Run pulls this container and deploys it as a serverless service, automatically handling scaling and infrastructure management.
- Build agent into Docker container
- Store container in Artifact Registry
- Deploy to Cloud Run with proper configuration
The gemini.md file serves as your agent's long-term memory and rulebook. It contains persistent instructions, coding guidelines, and best practices that influence your agent's behavior across all interactions.
When the Gemini CLI is invoked, it automatically loads this file's contents into its working memory. This ensures consistent application of your standards without needing to repeat instructions for every command.
- Contains coding standards and best practices
- Loaded automatically by the Gemini CLI
- Provides persistent context across sessions
Connecting tools to the Gemini CLI involves setting up MCP servers and configuring the CLI's settings.json file. This process creates a bridge between natural language commands and concrete tool actions.
First, you set up the external service (like a Git server or image generator). Then you configure an MCP server to interface with it. Finally, you update settings.json to make the CLI aware of this new capability.
- Set up the external tool or service
- Configure an MCP server as an adapter
- Update settings.json to enable the connection
GrowwStacks specializes in implementing custom AI agents and automation solutions for businesses. Our team has extensive experience with Gemini CLI, Agent Development Kit, and the entire Google Cloud AI stack.
We can help you design, build, and deploy AI agents tailored to your specific business needs. Whether you need workflow automation, intelligent assistants, or complex AI integrations, we'll create a solution that delivers real value.
- Custom AI agent development
- Integration with your existing systems
- Free consultation to discuss your requirements
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Manual AI implementations often fail to deliver real business value. With GrowwStacks, you'll get a production-ready AI agent deployed in days, not months.