Google Agent Development Kit (ADK) Introduction (3): Building a Multi-Agent Project Management System

Learning Objectives

• Master Layered Agent Architecture: Learn how to design and implement a hierarchical agent system for real-world collaboration.
• Implement Task Delegation and Coordination: See how agents can assign, accept, and complete tasks collaboratively.
• Build Dynamic Load Balancing: Ensure optimal workload distribution among agents to maximize efficiency.
• Technical Breadth: Compare agent-based project management with traditional monolithic approaches.

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System Architecture and Project Structure

This project uses the Google ADK (Agent Development Kit) to build a multi-agent project management system, simulating a real-world team with three specialized agent types:

• Manager Agent: Oversees the project, assigns tasks, and reviews progress.
• Engineer Agents: Implement assigned tasks and report their status.
• Tester Agents: Test completed tasks and provide feedback.

These agents are coordinated by a workflow engine, forming a complete project management workflow.

multi_agent_pm/
├── __init__.py
├── __main__.py         # Entry point for running as a module
├── main.py             # Main system implementation
├── common.py           # Shared data models and utilities
├── ai/                 # Natural language processing with Gemini
│   ├── __init__.py
│   ├── agent_nlp_handler.py
│   └── gemini_client.py
├── agents/             # Agent implementations
│   ├── __init__.py
│   ├── engineer/
│   ├── manager/
│   └── tester/
├── workflow/           # Workflow coordination
│   ├── __init__.py
│   └── coordinator.py
└── requirements.txt    # Project dependencies

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Agent Implementation and Collaboration

Manager Agent

• Assigns tasks to engineers based on workload.
• Reviews completed and tested tasks.
• Monitors overall project progress.

Engineer Agents

• Accept and work on assigned tasks.
• Mark tasks as completed when done.
• Communicate progress and issues.

Tester Agents

• Pick up completed tasks for testing.
• Submit test results and feedback.
• Help ensure quality before final approval.

Each agent exposes a REST API (as defined in their .well-known/agent.json manifest) and can also be interacted with via natural language commands, thanks to Gemini AI integration.

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Dynamic Load Balancing

The system features an automatic load balancing mechanism:

1. Monitor Agent Workloads: Regularly checks each agent’s task queue.
2. Redistribute Tasks: Moves tasks from overloaded agents to those with spare capacity.
3. Adapt to Change: Handles new tasks, agent failures, and shifting priorities in real time.

This ensures that no single agent becomes a bottleneck, and the project progresses smoothly.

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Natural Language Understanding

With Google Gemini integration, you can interact with the system using plain English:

• “Create a new high-priority task for payment integration.”
• “Show me all tasks assigned to engineers.”
• “Reassign the authentication task to Engineer2.”

Gemini parses these commands, extracts intent and parameters, and routes them to the appropriate agent and API.

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A2A Protocol and Agent Manifests

Each agent follows the Agent-to-Agent (A2A) protocol:

• Manifest File: .well-known/agent.json describes each agent’s capabilities, API endpoints, and schemas.
• Standardized Communication: Ensures agents can discover and interact with each other seamlessly.
• Extensibility: New agent types can be added by defining their manifest and implementing the required APIs.

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How to Run This Project

1. Install dependencies:

pip install -r requirements.txt

2. Set up Gemini (optional, for NLU):

• Get your API key from Google AI Studio
• Add it to a .env file:

  GEMINI_API_KEY=your_api_key_here

3. Run the system:

python -m multi_agent_pm

4. Interact via CLI or REST API:

• Use natural language commands in the CLI.
• Or, send HTTP requests to the agent endpoints (see README for details).

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What You’ll Learn

• Agent System Design: How to break down complex workflows into specialized, cooperating agents.
• Task Delegation and Coordination: Implementing real-world project management logic in code.
• Dynamic Load Balancing: Keeping your system efficient and responsive.
• Natural Language Integration: Making your system user-friendly and AI-powered. (Actually, this part wasn't done very well, let's talk about it later.)
• ADK Best Practices: Using manifests and protocols for scalable, maintainable agent systems.

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Conclusion

Building a multi-agent project management system with Google ADK is a powerful way to learn about modern, distributed, and intelligent software architecture.

You’ll gain hands-on experience with agent design, coordination, and AI integration—skills that are increasingly valuable in today’s tech landscape.

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For complete code and examples, see the GitHub repository.