Building a Collaborative Agent2Agent AI Framework Using Google Gemini Models

Introduction to Agent2Agent Framework

This tutorial demonstrates the implementation of an Agent2Agent collaborative framework leveraging Google’s Gemini models. The framework creates specialized AI personas such as data scientists, product strategists, risk analysts, and creative innovators, enabling them to communicate and collaborate on complex problem-solving tasks.

Defining Roles and Communication Protocols

By establishing clear roles, personalities, and messaging protocols, the framework orchestrates multi-agent interactions in three distinct phases: individual analysis, cross-agent critique, and synthesis of solutions.

Core Components and Message Types

The system uses an enumeration of message types to structure communication: handshake, task proposal, analysis, critique, synthesis, vote, and consensus. A dataclass, A2AMessage, encapsulates message metadata including sender, receiver, type, payload, timestamp, and priority.

GeminiAgent Class

Each GeminiAgent instance represents an AI persona with attributes like agent ID, role, personality, and temperature controlling response variability. The agent generates structured JSON responses using Google Gemini’s generative models, applying methods for task analysis, critique, and solution synthesis.

Collaborative System Orchestration

The Agent2AgentCollaborativeSystem class manages multiple GeminiAgent instances, facilitating a four-phase workflow:

1. Individual Agent Analysis
2. Cross-Agent Critique and Feedback
3. Solution Synthesis
4. Consensus and Recommendation

This system prints detailed logs and returns final proposed solutions, highlighting the agent with the most confident solution.

Creating Specialized Agents

A function creates a balanced team of five agents, each with a unique domain expertise and personality:

• Data Scientist & Analytics Specialist
• Product Strategy & User Experience Expert
• Technical Architecture & Engineering Lead
• Innovation & Creative Problem Solving Specialist
• Risk Management & Compliance Expert

Running the Demo

The demo function sets up the environment, registers agents, and runs collaborative problem-solving sessions on complex challenges like sustainable urban transportation and AI-powered healthcare diagnostics strategies.

This step-by-step implementation showcases how Google Gemini models enable rich AI-to-AI collaboration, producing data-driven, consensus-based solutions for multifaceted problems. The modular design allows easy extension with new roles or message types for diverse application domains.