How to Build AI Agents from Scratch with Google ADK ( Tutorial)
Most businesses struggle with basic chatbots that can't access live data or take actions. Google's Agent Development Kit (ADK) transforms standard LLMs into powerful AI agents that can fetch real-time information, enforce safety rules, and orchestrate complex workflows - all with production-ready tooling.
The Critical Limitations of Basic LLMs
Traditional large language models like ChatGPT hit a wall when you need real-time data access or action-taking capabilities. Try asking a standard LLM to "fetch the latest blog from my database" - it will respond that it can't access private systems. This limitation stems from LLMs being trained on static datasets with no connection to live data sources.
The breakthrough comes when we combine LLMs with the ability to execute API calls. This transforms them from passive text generators into active agents that can retrieve information, process it, and take actions on your behalf. Google's ADK provides the framework to build these enhanced capabilities systematically.
Key insight: Basic LLMs are like librarians who can only reference books in their library. AI agents are like research assistants who can call experts, search the web, and compile reports from multiple sources.
AI Agent Architecture Explained
An effective AI agent combines several components working together:
- LLM Core: Handles natural language understanding and reasoning (Gemini 2.5 Flash in our example)
- Tools: Enable API calls to external services (like weather APIs or database queries)
- Guardrails: Safety controls that limit data access and prevent harmful actions
- Orchestration: Manages communication between multiple specialized agents
The Google ADK provides pre-built implementations for all these components, dramatically reducing development time. At 12:35 in the tutorial, you'll see how the ADK's session management handles conversation history and tool outputs automatically.
Setting Up Google ADK
Getting started requires just a few steps:
Prerequisites: You'll need a Google Cloud account with billing enabled and a Gemini API key from AI Studio. The free tier provides ample resources for development.
Initialize a TypeScript project and install the ADK packages:
pnpm init pnpm install @google-cloud/adk @google-cloud/adk-dev-tools pnpm install typescript ts-node --save-dev Create a .env file with your Gemini API key:
GEMINI_API_KEY=your_api_key_here Building Your First Agent
Let's create a simple "Hello World" agent to understand the core concepts. The ADK makes this surprisingly straightforward:
import { LmAgent } from '@google-cloud/adk'; export const helloAgent = new LmAgent({ name: 'HelloAgent', model: 'gemini-2.5-flash', description: 'Basic greeting agent', instruction: 'You are a smart prompter who always replies with "Hello [username]"' }); This agent demonstrates three key ADK features:
- Declarative configuration: Define agent behavior through properties rather than complex code
- Model selection: Choose different Gemini models based on task complexity
- Instruction tuning: Guide the agent's behavior with natural language instructions
Adding API Tools to Your Agent
The real power comes when we give our agent access to external data. Here's how to create a weather agent that fetches live forecasts:
import { FunctionalTool } from '@google-cloud/adk'; import { z } from 'zod'; const weatherTool = new FunctionalTool({ name: 'getCurrentWeather', description: 'Fetches weather data for a specified city', parameters: z.object({ city: z.string().describe('City name for weather lookup') }), execute: async ({ city }) => { const response = await fetchWeatherAPI(city); return { status: 'success', data: response }; } }); Key aspects of tool implementation:
- Input validation: Zod ensures correct parameters before execution
- Error handling: Structured responses for success/failure cases
- Type safety: Full TypeScript support throughout the toolchain
Creating Multi-Agent Systems
For complex tasks, you'll want multiple specialized agents working together. The ADK makes this manageable with its boss agent pattern:
const bossAgent = new LmAgent({ name: 'BossAgent', model: 'gemini-2.5-flash', description: 'Routes requests to appropriate sub-agents', instruction: `You are a dispatcher that routes user requests: - Weather queries → WeatherAgent - Greetings → HelloAgent - Unknown requests → DefaultAgent` }); At 38:20 in the video, you'll see a live demo of this multi-agent system handling a travel booking scenario with three specialized agents coordinating seamlessly.
Production tip: Start with single-purpose agents, then gradually introduce orchestration as your needs grow. The ADK's session tracing makes debugging complex interactions manageable.
Watch the Full Tutorial
See the complete implementation from basic agents to multi-agent orchestration in this 56-minute tutorial. At 22:45, you'll get a particularly useful look at the ADK DevTools for debugging agent behavior.
Key Takeaways
AI agents represent the next evolution beyond basic chatbots, combining LLM reasoning with real-world action capabilities. Google's ADK provides the fastest path to production-ready agents with:
- Pre-built components for tools, guardrails, and orchestration
- TypeScript-first development experience
- Comprehensive debugging and monitoring tools
- Scalable architecture for both simple and complex agent systems
In summary: With Google ADK, you can build agents that don't just talk - they act, fetch live data, and collaborate to solve real business problems.
Frequently Asked Questions
Common questions about this topic
An LLM (like ChatGPT) is limited to its training data, while an AI agent combines an LLM with tools like API access, guardrails, and orchestration capabilities. The key difference is that agents can perform actions like fetching live data from your databases or external APIs, while basic LLMs can only respond based on their training.
Agents maintain context across conversations and can chain multiple actions together to complete complex tasks. They're essentially LLMs augmented with the ability to interact with the world beyond their training data.
- Basic LLM: Answers based on pre-trained knowledge
- AI Agent: Can access live systems, enforce rules, and orchestrate workflows
- Key Advantage: Agents solve real business problems requiring current data or actions
Google ADK provides pre-built components for agent development including session management, tool integration, and debugging tools. It saves hundreds of hours compared to building from scratch. The ADK handles complex aspects like event streaming, session persistence, and multi-agent communication protocols.
The kit includes production-ready implementations of common agent patterns, allowing developers to focus on business logic rather than infrastructure. Google maintains and updates the ADK as agent technology evolves.
- Time savings: 70-80% reduction in boilerplate code
- Built-in best practices: Security, scalability, and observability
- Future-proof: Regular updates from Google's AI team
The four core components are: 1) LLM for reasoning, 2) Tools for API access/actions, 3) Guardrails for safety controls, and 4) Orchestration for multi-agent workflows. A basic agent needs at least an LLM and tools, while production agents require all four components.
Google ADK provides implementations for each component with sensible defaults. For example, tools include built-in retry logic and error handling, while guardrails offer configurable data access policies.
- LLM: Gemini models for reasoning/natural language
- Tools: Pre-built connectors for common APIs
- Guardrails: Configurable safety and compliance rules
Agents use guardrails - rules that limit what data can be accessed and how it can be used. For example, you might configure a financial agent to only access transaction data from the last 30 days, or a healthcare agent to redact PHI before processing. Google ADK includes built-in guardrail templates.
The ADK implements guardrails at multiple levels: tool-level permissions, data masking in responses, and audit logging of all agent actions. This layered approach meets most compliance requirements out of the box.
- Data minimization: Only access necessary fields
- Masking: Automatically redact sensitive information
- Audit trails: Full logs of all agent actions
Yes, multi-agent orchestration allows specialized agents to collaborate. For example, a travel booking might involve: 1) Location agent finds destinations, 2) Flight agent checks availability, 3) Pricing agent compares options, and 4) Booking agent completes purchase. The ADK provides session sharing and event routing for such workflows.
The boss agent pattern demonstrated in the tutorial shows how to coordinate multiple agents while maintaining a simple user interface. Users interact naturally while the agent system handles the complexity behind the scenes.
- Specialization: Each agent focuses on one capability
- Coordination: Shared session context between agents
- Scalability: Add new agents without disrupting existing ones
The Google ADK currently has first-class support for TypeScript/JavaScript and Python. The TypeScript implementation is particularly robust, including type-safe tool definitions, protocol buffers for inter-agent communication, and comprehensive debugging tools in the ADK DevTools.
While other languages may work through REST APIs, Google recommends TypeScript for the best developer experience and access to all ADK features. The TypeScript SDK receives updates and new features first.
- Primary: TypeScript (recommended), Python
- Secondary: REST API for other languages
- Key advantage: Full type safety in TypeScript implementation
Costs vary based on LLM choice (Gemini Flash vs. Ultra), query volume, and tool usage. A simple weather agent might cost $5-10/month for light usage, while complex multi-agent systems can run $500+/month. The ADK itself is free - you only pay for the underlying LLM API calls and any cloud resources your tools require.
Google's pricing calculator helps estimate costs based on expected usage patterns. Many businesses find the ROI compelling, as agents can automate tasks that would otherwise require human labor.
- LLM costs: $0.50-$5 per 1000 queries depending on model
- Tool costs: Varies by API (many have free tiers)
- Savings: Often replaces $X,XXX/month in labor costs
GrowwStacks specializes in building custom AI agent systems using Google ADK and other frameworks. We handle the complete lifecycle: 1) Identifying automation opportunities, 2) Designing agent architectures, 3) Developing and testing agents, and 4) Deploying with proper guardrails.
Our team has implemented agents for customer support, data analysis, workflow automation, and more. We offer both implementation services and training to help your team build agent expertise in-house.
- Custom agent development: Tailored to your specific needs
- Integration services: Connect with your existing systems
- Free consultation: Discuss your agent strategy with our experts
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