I Built a Multi-Step AI Agent in One Day with Google ADK — Here's What Nobody Tells You

I'm a final-year computer science student. I spend most of my days training deep learning models on image datasets, debugging tensor shape errors at 2am, and convincing myself that 67% accuracy is "a solid baseline."

I do not, normally, build AI agents.

But when Google Cloud NEXT '26 dropped last week and I saw the announcements around ADK 2.0 and the new Gemini Enterprise Agent Platform, I got genuinely curious. Not marketing-brochure curious — actually curious. Because the thing they kept saying was: "You can now build multi-step autonomous agents that coordinate with each other."

That sounded either really powerful or really overhyped. I wanted to find out which.

So I spent a day building something with it. This is what actually happened.

What Even Is ADK?

Before I get into the friction, a quick explainer for anyone who hasn't seen the announcements.

ADK — Agent Development Kit — is Google's open-source Python framework for building AI agents. Not chatbots. Agents — programs that take a goal, break it into steps, use tools, and figure out how to get things done autonomously.

The ADK 2.0 alpha (released March 2026) brought in graph-based workflows, collaborative multi-agent support, and native Vertex AI integration. The stable version (1.x) already supports multi-agent coordination and tool use. That's what I ended up using, and I'll explain why in a moment.

What I Decided to Build

I wanted to build a Research Assistant Agent — you give it a topic, it searches the web, structures the findings, and suggests what to explore next.

The twist: instead of one agent doing everything, I'd build it as a multi-agent pipeline with specialist sub-agents, the way ADK is actually designed to be used:

• 
  web_searcher → hits Google Search, returns raw findings
  


• 
  analyst_summarizer → structures those findings for developers
  


• 
  research_coordinator → orchestrates both, delivers the final answer
  

Simple enough concept. Let's talk about what happened when I actually tried to set it up.

The Setup: Where Things Got Interesting

Step 1 — Getting the API Key

Go to

Research-Assistant-Agent/

├── Research-Assistant-Agent/

│   ├── agent.py        ← all the agent logic

│   └── __init__.py

├── .env.example        ← copy this to .env, add your key

├── .gitignore

└── README.md

To run it yourself:

git clone https://github.com/SimranShaikh20/Research-Assistant-Agent

cd Research-Assistant-Agent

python -m venv venv

venv\Scripts\activate        # Windows

# source venv/bin/activate   # Mac/Linux

pip install google-adk

# copy .env.example to .env, add your Gemini API key

adk web research_agent/

Resources

• on DEV Community. I'm a final-year BE Computer Science student at The Maharaja Sayajirao University of Baroda, where my major project is an AI-based defect detection system — so building agents like this is a bit of a departure from my usual ResNet-50 territory. Turned out to be worth the detour.
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