Research & Summarization Multi-Agent : Practical Workflows with Google ADK and Gemini

Introduction

AI agents are superstars at automating work, but when tasks get complicated, a single “do-it-all” agent quickly runs into limits. That’s where multi-agent systems shine. With Google’s Agent Development Kit (ADK), creating specialized, collaborative agent teams is both accessible and powerful.

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Why Build Multi-Agent Systems?

Imagine a monolithic agent trying to do research, summarize findings, write, and fact-check—all in one prompt. That’s a recipe for confusion and errors! Multi-agent architectures solve this by splitting work among focused, “expert” agents:

• Easier to build and debug

• Specialized for reliability

• Greater transparency and scalability

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Research & Summarization Multi-Agent:

Let's build a system with two specialized agents:

1. Research Agent - Searches for information using Google Search

2. Summarizer Agent - Creates concise summaries from research findings

3. Coordinator Agent: Orchestrates the sequence, delegating each sub-task to the appropriate agent.

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Architecture:

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Agent Code:

Code hosted on my GitHub repository Clone or fork the repo to run this research summarization agent.

This code has been successfully run and tested in a Google Colab notebook. To execute it, set up the API key using the GOOGLE_API_KEY variable in your Colab environment.

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Final Execution Output:

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Final Thoughts & Next Steps

Multi-agent systems will shape the future of practical, team-based AI automation. With Google ADK, you can prototype, iterate, and scale agent workflows rapidly—whether for research pipelines, content generation, or real-world industry automation.

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References & Further Reading

• Google ADK Documentation

• Kaggle