Artificial Intelligence is shifting rapidly — from tools that simply respond to inputs, to intelligent agents that operate semi-independently, delegate tasks, and even coordinate with other agents. If you’re even remotely interested in building multi-agent AI systems, Google’s Agent Development Kit (ADK) is worth your time.
Whether you’re just starting out or already familiar with AI development, ADK offers a modular, open-source foundation to fast-track your progress.
Let’s explore how.
What Is ADK?
The Agent Development Kit (ADK) is an open-source framework created by Google that enables developers to build, deploy, and evaluate intelligent agents — autonomous entities powered by language models that can perform tasks and make decisions.
ADK is built on Python and supports both single-agent and multi-agent workflows. It abstracts the complexity of managing logic, conversations, and inter-agent communication, making it easier to move from prototyping to production.
Core Components of ADK
1. LLM Agents
These agents are wrappers around large language models (LLMs). You can configure the prompt, context window, and behavior.
from google.adk.agents import LlmAgent
agent = LlmAgent(
model="gemini-2.0-pro",
name="qa_agent",
description="Answers questions based on a knowledge base",
instruction="Provide accurate, concise answers to user questions"
)
LLM agents abstract away the boilerplate of token handling, context formatting, and retry logic. They support model-specific configurations and can even call tools dynamically (via ReAct-style prompting or function-calling).
2. Workflow Agents
Workflow agents are meta-agents: they coordinate the execution of multiple agents and tools using a control flow.
You define tasks and dependencies in a DAG-like structure, including logic like conditional branching or retries.
from google.adk.workflow import SequentialWorkflow
workflow = SequentialWorkflow(
name="document_summary",
steps=[
{"name": "extract_text", "agent": extract_agent},
{"name": "summarize", "agent": summarizer_agent},
{"name": "store", "agent": storage_agent}
]
)
This is incredibly useful in enterprise environments, where business logic is complex and requires deterministic execution pipelines.
3. Custom Agents
You can subclass BaseAgent or ToolAgent to implement custom behavior. This is useful when:
- You want to integrate a non-LLM backend (e.g., a database or an API).
- You need agents with fine-tuned decision-making logic.
- You want to embed domain-specific tools directly.
from google.adk.core import ToolAgent
class WeatherAgent(ToolAgent):
def run(self, input_data):
location = self.extract_location(input_data)
return self.call_weather_api(location)
Agent-to-Agent Communication (A2A)
A powerful feature in ADK is A2A messaging, which allows agents to delegate tasks to other agents. This is made possible through Google’s A2A protocol, which standardizes message formats, agent interfaces, and routing.
Why this matters:
- Agents can collaborate without hardcoding interactions.
- It enables emergent behavior in multi-agent systems.
- You can architect distributed agent systems, potentially across microservices.
In practice, this is achieved via message queues or HTTP endpoints, with serialized payloads that include intent, metadata, and task parameters.
Example:
{
"sender": "travel_planner",
"receiver": "hotel_booking",
"intent": "book_room",
"params": {
"city": "Paris",
"dates": ["2025-05-01", "2025-05-07"]
}
}
Evaluation and Tracing
ADK includes built-in evaluation tools that log and trace every agent decision. This is essential for debugging multi-agent systems, where the complexity can quickly become unmanageable.
Key features:
- Evaluation hooks to track input/output, latency, and errors
- Visual traces of workflows
- Custom metrics support for task success rates, fallback rates, etc.
These features integrate well with Google Cloud Logging and other observability platforms, making it viable for production deployment.
Deployment Options
ADK agents are deployable in several environments:
- Locally: Via the built-in
adk webcommand, great for prototyping - Cloud-native: Deploy via Docker, Kubernetes, or Google Cloud Run
- Hybrid: Run agents locally and coordinate through cloud A2A endpoints
Because agents are stateless by default, horizontal scaling is straightforward. You can scale up the number of agents or duplicate specific agents to handle load.
Why You Should Learn ADK
Understanding ADK is a future-proof investment. Here’s why:
- It abstracts complexity: You don’t have to reinvent the wheel every time you want agents to talk, act, or collaborate.
- It’s model-agnostic: Use Google’s Gemini or plug in your favorite LLM.
- It’s scalable: Easily move from a prototype to a distributed architecture.
- It aligns with where the industry is headed: Agent-based systems are already showing up in workflows, productivity apps, and enterprise software.
ADK essentially gives you a design system for intelligent agents — and that’s a big deal.
Source Credit: https://medium.com/google-cloud/from-hello-world-to-hello-agent-googles-adk-made-easy-02a21bb9ce75?source=rss—-e52cf94d98af—4
