No-Code AI: Building Autonomous Agents with Google ADK and YAML

Learn how to build and deploy powerful AI agents using the Google Agent Development Kit (ADK) without writing a single line of Python code, using YAML-based configurations.

Posted on: 2026-02-28 by AI Assistant

Introduction

The barrier to entry for building sophisticated AI agents is falling. While the Google Agent Development Kit (ADK) offers deep programmatic control for developers, it also introduces a powerful “Agent Config” feature. This allows anyone to assemble and run an autonomous agent using simple YAML configuration files—no Python code required.

In this post, we’ll explore how to get started with no-code ADK agents, from basic setup to orchestrating complex multi-agent systems.

Getting Started: The adk CLI

The journey begins with the ADK command-line interface. Once you have the ADK Python libraries installed, you can scaffold a new no-code project with a single command:

adk create --type=config my_no_code_agent

This command creates a directory structure containing two essential files:

• root_agent.yaml: The heart of your agent’s definition.
• .env: Where you securely store your Gemini API keys or Google Cloud credentials.

Defining Your Agent in YAML

The root_agent.yaml file is where you define your agent’s identity and behavior. A basic configuration looks like this:

# yaml-language-server: $schema=https://raw.githubusercontent.com/google/adk-python/refs/heads/main/src/google/adk/agents/config_schemas/AgentConfig.json
name: assistant_agent
model: gemini-2.5-flash
description: A helper agent that can answer users' questions.
instruction: You are an agent to help answer users' various questions.

By simply editing these fields, you can pivot your agent from a general assistant to a specialized tutor, a researcher, or a creative partner.

Extending Capabilities Without Code

The true power of the ADK lies in its extensibility. Even without writing code, you can grant your agent access to powerful tools and even other agents.

1. Built-in Tools

Want your agent to search the live web? Just add the google_search tool to your config:

name: search_agent
model: gemini-2.0-flash
tools:
  - name: google_search

2. Multi-Agent Systems

You can create a “manager” agent that delegates tasks to specialized sub-agents. This is handled entirely within the YAML structure by pointing to other configuration files:

name: manager_agent
instruction: |
  Delegate coding questions to the code_tutor and math questions to the math_tutor.
sub_agents:
  - config_path: code_tutor_agent.yaml
  - config_path: math_tutor_agent.yaml

Running Your Agent

Once your YAML files are ready, the ADK provides three distinct ways to interact with your creation:

• adk web: Launches a sleek web-based chat interface.
• adk run: Starts a text-only interaction directly in your terminal.
• adk api_server: Deploys your agent as a service that other applications can talk to.

Conclusion

The ADK’s Agent Config feature represents a shift towards “Config-as-Agent.” It empowers domain experts—who might not be programmers—to design complex AI workflows. Whether you’re building a simple search bot or a hierarchical team of specialized assistants, the path from idea to execution is now just a few lines of YAML away.