AI Integration
The complete machine-readable reference is at https://docs.boozang.com/agent/AGENT.md — start there for concepts, workflows, and the full MCP API with parameters.
Boozang provides multiple ways for AI assistants to help with test automation.
Setup with bz-agent
The bz-agent CLI tool is the recommended way to connect AI assistants to your Boozang project:
npx bz-agent init
This creates a .bz/ directory with:
AGENT.md— Entry point for AI assistants (give them this file)config.json— Server URL, API endpoint, version.env— Auth token (gitignored)mcp-api.md— Full API reference with parameters for all 40+ toolsconcepts.md,workflows.md,best-practices.md— AI-readable guides
Cache your project structure so AI agents don't need to re-fetch it:
npx bz-agent snapshot # Creates .bz/project-map.md and modules-cache.json
Parse Boozang runner logs for test results and failures:
npx bz-agent parse worker1.log --pretty
npx bz-agent parse ./logs/ --build=1866
Conversation-Based Assistance
The simplest way to use AI with Boozang is through conversation. AI assistants can:
Help Plan Tests
Describe your application and user journeys. The AI will suggest:
- How to structure modules
- Which test types to use (functional, API, visual)
- Data-driven testing strategies
- Edge cases to consider
Guide Test Creation
While working in Boozang, ask the AI for help with:
- Element selection strategies
- Handling dynamic content
- Validation approaches
- Control flow (loops, conditionals)
Debug Failures
When tests fail, share the error details. The AI can:
- Analyze failure patterns
- Suggest root causes
- Recommend fixes
- Identify flaky test indicators
IDE Integration
Modern IDEs with AI capabilities (Cursor, VS Code with Copilot) can assist when working with Boozang's coded tests or CI configurations.
Coded Tests
For teams using Boozang's coded test feature, AI can help write JavaScript test code that integrates with Boozang's API.
CI Configuration
AI can help configure:
- Jenkins pipelines
- GitHub Actions workflows
- GitLab CI configurations
- Docker-based test execution
MCP API Integration
The Model Context Protocol (MCP) API enables programmatic AI integration:
AI Assistant ←→ MCP API ←→ Boozang Platform
Authentication uses project-scoped tokens (prefixed bzmcp_) with two scopes:
read— Browse projects, modules, tests, environments, IDE statewrite— All read operations plus create, edit, delete, and IDE control
The API provides 40+ tools across six categories:
- Module/Test/Action CRUD — Full lifecycle management
- Environment management — Create, edit, provision environments
- IDE control — Navigate, run tests, get screenshots, set breakpoints
- Auth configuration — Manage authentication settings
See the MCP API Reference for an overview, or the full specification with parameters for complete details.
Best Practices for AI-Assisted Testing
Be Specific
Instead of: "Create tests for my app"
Try: "Create a login test module with tests for: valid login, invalid password, forgot password flow"
Provide Context
Share relevant information:
- Application URL and technology stack
- User roles and permissions
- Business rules and validation requirements
- Known edge cases
Iterate
AI-generated tests are a starting point. Review, run, and refine:
- Generate initial test structure
- Run tests to verify they work
- Adjust selectors or assertions as needed
- Add data variations
Maintain Human Oversight
AI accelerates test creation but doesn't replace judgment:
- Review generated tests for correctness
- Validate business logic coverage
- Ensure security-sensitive flows are properly tested
- Monitor test reliability over time
Supported AI Assistants
Boozang works with any AI assistant that can read the .bz/AGENT.md guide and make HTTP requests to the MCP API. The bz-agent init command optionally creates a CLAUDE.md pointer file for Claude Code.
Popular options include:
- Claude Code - Anthropic's CLI agent (reads CLAUDE.md automatically)
- Cursor - AI-powered code editor
- ChatGPT - OpenAI's conversational AI
- GitHub Copilot - AI pair programmer