0b61175d98
* feat: transform QA agent into Test Architect with advanced quality capabilities - Add 6 specialized quality assessment commands - Implement risk-based testing with scoring - Create quality gate system with deterministic decisions - Add comprehensive test design and NFR validation - Update documentation with stage-based workflow integration * feat: transform QA agent into Test Architect with advanced quality capabilities - Add 6 specialized quality assessment commands - Implement risk-based testing with scoring - Create quality gate system with deterministic decisions - Add comprehensive test design and NFR validation - Update documentation with stage-based workflow integration * docs: refined the docs for test architect * fix: addressed review comments from manjaroblack, round 1 * fix: addressed review comments from manjaroblack, round 1 --------- Co-authored-by: Murat Ozcan <murat@mac.lan> Co-authored-by: Brian <bmadcode@gmail.com>
110 lines
5.2 KiB
Markdown
110 lines
5.2 KiB
Markdown
# BMad Expansion Pack: Google Cloud Vertex AI Agent System
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[](https://opensource.org/licenses/MIT)
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[](https://www.google.com/search?q=https://github.com/antmikinka/BMAD-METHOD)
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[](https://cloud.google.com/)
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This expansion pack provides a complete, deployable starter kit for building and hosting sophisticated AI agent systems on Google Cloud Platform (GCP). It bridges the gap between the BMad Method's natural language framework and a production-ready cloud environment, leveraging Google Vertex AI, Cloud Run, and the Google Agent Development Kit (ADK).
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## Features
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- **Automated GCP Setup**: `gcloud` scripts to configure your project, service accounts, and required APIs in minutes.
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- **Production-Ready Deployment**: Includes a `Dockerfile` and `cloudbuild.yaml` for easy, repeatable deployments to Google Cloud Run.
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- **Rich Template Library**: A comprehensive set of BMad-compatible templates for Teams, Agents, Tasks, Workflows, Documents, and Checklists.
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- **Pre-configured Agent Roles**: Includes powerful master templates for key agent archetypes like Orchestrators and Specialists.
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- **Highly Customizable**: Easily adapt the entire system with company-specific variables and industry-specific configurations.
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- **Powered by Google ADK**: Built on the official Google Agent Development Kit for robust and native integration with Vertex AI services.
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## Prerequisites
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Before you begin, ensure you have the following installed and configured:
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- A Google Cloud Platform (GCP) Account with an active billing account.
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- The [Google Cloud SDK (`gcloud` CLI)](<https://www.google.com/search?q=%5Bhttps://cloud.google.com/sdk/docs/install%5D(https://cloud.google.com/sdk/docs/install)>) installed and authenticated.
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- [Docker](https://www.docker.com/products/docker-desktop/) installed on your local machine.
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- Python 3.11+
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## Quick Start Guide
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Follow these steps to get your own AI agent system running on Google Cloud.
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### 1\. Configure Setup Variables
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The setup scripts use placeholder variables. Before running them, open the files in the `/scripts` directory and replace the following placeholders with your own values:
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- `{{PROJECT_ID}}`: Your unique Google Cloud project ID.
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- `{{COMPANY_NAME}}`: Your company or project name (used for naming resources).
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- `{{LOCATION}}`: The GCP region you want to deploy to (e.g., `us-central1`).
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### 2\. Run the GCP Setup Scripts
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Execute the setup scripts to prepare your Google Cloud environment.
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```bash
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# Navigate to the scripts directory
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cd scripts/
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# Run the project configuration script
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sh 1-initial-project-config.sh
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# Run the service account setup script
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sh 2-service-account-setup.sh
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```
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These scripts will enable the necessary APIs, create a service account, assign permissions, and download a JSON key file required for authentication.
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### 3\. Install Python Dependencies
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Install the required Python packages for the application.
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```bash
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# From the root of the expansion pack
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pip install -r requirements.txt
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```
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### 4\. Deploy to Cloud Run
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Deploy the entire agent system as a serverless application using Cloud Build.
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```bash
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# From the root of the expansion pack
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gcloud builds submit --config deployment/cloudbuild.yaml .
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```
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This command will build the Docker container, push it to the Google Container Registry, and deploy it to Cloud Run. Your agent system is now live\!
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## How to Use
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Once deployed, the power of this system lies in its natural language templates.
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1. **Define Your Organization**: Go to `/templates/teams` and use the templates to define your agent teams (e.g., Product Development, Operations).
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2. **Customize Your Agents**: In `/templates/agents`, use the `Master-Agent-Template.yaml` to create new agents or customize the existing Orchestrator and Specialist templates. Define their personas, skills, and commands in plain English.
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3. **Build Your Workflows**: In `/templates/workflows`, link agents and tasks together to create complex, automated processes.
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The deployed application reads these YAML and Markdown files to dynamically construct and run your AI workforce. When you update a template, your live agents automatically adopt the new behaviors.
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## What's Included
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This expansion pack has a comprehensive structure to get you started:
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```
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/
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├── deployment/ # Dockerfile and cloudbuild.yaml for deployment
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├── scripts/ # GCP setup scripts (project config, service accounts)
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├── src/ # Python source code (main.py, settings.py)
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├── templates/
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│ ├── agents/ # Master, Orchestrator, Specialist agent templates
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│ ├── teams/ # Team structure templates
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│ ├── tasks/ # Generic and specialized task templates
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│ ├── documents/ # Document and report templates
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│ ├── checklists/ # Quality validation checklists
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│ ├── workflows/ # Workflow definition templates
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│ └── ...and more
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├── config/ # Customization guides and variable files
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└── requirements.txt # Python package dependencies
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```
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## Contributing
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Contributions are welcome\! Please follow the main project's `CONTRIBUTING.md` guidelines. For major changes or new features for this expansion pack, please open an issue or discussion first.
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