Real-World Agent Examples with Gemini 3

Gemini 3 is powering the next generation of reliable, production-ready AI agents. This post highlights 6 open-source framework collaborations (ADK, Agno, Browser Use, Eigent, Letta, mem0), demonstrating practical agentic workflows for tasks like deep search, multi-agent systems, browser and enterprise automation, and stateful agents with advanced memory. Clone the examples and start building today.

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Google
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Philipp Schmid, Mark McDonald, Vishal Dharmadhikari
•5 min read•intermediate•
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Overview

The article discusses the advancements in agentic AI with Gemini 3, emphasizing its role as a core orchestrator for complex workflows. It showcases real-world applications through collaborations with open-source frameworks, providing developers with practical examples to clone and customize.

What You'll Learn

1

How to build scalable AI agents using the Agent Development Kit (ADK)

2

Why using multi-agent systems can enhance data processing capabilities

3

How to automate web interactions with Browser Use library

4

When to implement memory management in AI agents with Letta

Prerequisites & Requirements

  • Understanding of AI agent architecture and workflows
  • Familiarity with open-source frameworks for AI development(optional)

Key Questions Answered

What is the Agent Development Kit (ADK) and how does it facilitate AI agent development?
The Agent Development Kit (ADK) is an open-source, model-agnostic framework developed by Google that simplifies the process of building, testing, and deploying AI agents. It provides essential architectural primitives for creating scalable workflows, supporting any large language model (LLM) while maximizing the capabilities of the Gemini family of models.
How does the Browser Use library enhance web automation for AI agents?
The Browser Use library allows AI agents to interact with websites by bridging the gap between LLM reasoning and actual browser actions. It enables agents to visually identify fields and autonomously handle complex inputs, ensuring fluid and reliable automation, especially in multi-step forms.
What role does memory management play in AI agents built with Letta?
Letta introduces a memory hierarchy concept that allows AI agents to manage their context effectively. This enables agents to maintain persistent memory that evolves through interactions, ensuring they do not forget core instructions or history, which is crucial for developing stable personas.
What are the capabilities of the Agno framework in building multi-agent systems?
Agno is an open-source framework that supports the creation of multi-agent systems with memory and knowledge. It allows developers to build specialized agents that can autonomously query APIs and reason over data, showcasing its integration with Gemini 3 Pro for enhanced functionality.

Technologies & Tools

AI Model
Gemini 3
Serves as the core reasoning engine for various AI agents showcased in the article.
Framework
Agent Development Kit (adk)
Facilitates the development of scalable AI agents.
Framework
Agno
Supports the creation of multi-agent systems with memory and knowledge.
Library
Browser Use
Enables AI agents to interact with websites for automation tasks.
Platform
Letta
Provides advanced memory management for stateful AI agents.
Framework
Mem0
Offers a memory layer for AI applications to enhance personalization.

Key Actionable Insights

1
Explore the Agent Development Kit (ADK) to streamline your AI agent development process.
By leveraging the ADK, developers can create scalable workflows that integrate various AI capabilities, making it easier to build and deploy complex agents.
2
Utilize the Browser Use library to enhance your AI agents' web interaction capabilities.
This library allows agents to perform actions like form-filling and navigating websites more effectively, which is essential for automating tasks in real-world applications.
3
Implement memory management techniques using Letta to improve agent performance.
Effective memory management helps agents maintain context over long interactions, leading to more coherent and personalized user experiences.

Common Pitfalls

1
Neglecting the importance of memory management in AI agents can lead to context drift.
Without effective memory management, agents may forget critical information over time, resulting in inconsistent user interactions and a lack of coherence in responses.