This repository serves as a playground for experimenting with agentic AI concepts using the ADK Gemini framework. It includes tools and examples for building, testing, and deploying agent-based systems.
- Expense Management Automation: Automate expense approvals with AI.
- Human-in-the-Loop Approvals: Integrate human decision-making into automated workflows.
- Extensible Framework: Easily add new agent capabilities.
AgentOps, short for Agent Operations, refers to the practice of managing, deploying, and optimizing agent-based systems. It focuses on ensuring that AI agents operate efficiently, reliably, and in alignment with organizational goals. This includes:
- Lifecycle Management: Managing the development, deployment, and retirement of AI agents.
- Monitoring and Optimization: Tracking agent performance and improving their decision-making capabilities.
- Human Collaboration: Integrating human oversight and decision-making into agent workflows.
- Scalability: Ensuring agents can handle increasing workloads and adapt to new tasks.
- Automation: Streamline repetitive tasks and processes using intelligent agents.
- Human-in-the-Loop: Combine the strengths of AI and human expertise for critical decision-making.
- Adaptability: Quickly adapt to new workflows and integrate with existing systems.
- Transparency: Provide clear insights into agent decisions and actions.
- Collaboration: Enable seamless interaction between multiple agents and human operators.
- Python 3.12 or higher
- Clone the repository:
git clone https://github.com/divakarkumarp/adk-gemini-agentops-playground.git
- Navigate to the project directory:
cd adk-gemini-agentops-playground -
- Install dependencies using pip:
pip install -r requirements.txt
- Run the desired script:
python expense_mage.py
- Launch Jupyter Notebook:
jupyter notebook
- Open
expense_mage.pyto explore Expense Management System with Google ADK + AgentOps workflows.
This project is licensed under the MIT License. See the LICENSE file for details.
4e6f18f (This commit includes py and ipynb files , requirements.txt and readme file)
