# CI/CD Automation - API Specification

## Introduction

This API specification documents the programmatic interfaces for the CI/CD Automation module of Codomyrmex. The module provides comprehensive continuous integration and deployment capabilities, including pipeline management, automated testing, deployment orchestration, and build automation for the Codomyrmex ecosystem.

## Functions

### Function: `create_pipeline(name: str, stages: List[Dict], config: Optional[Dict] = None, **kwargs) -> Pipeline`

- **Description**: Create and configure a CI/CD pipeline with specified stages and configuration.
- **Parameters**:
    - `name`: Unique pipeline name identifier.
    - `stages`: List of pipeline stage configurations with jobs and tasks.
    - `config`: Optional pipeline-level configuration (triggers, environments, etc.).
    - `**kwargs`: Additional pipeline configuration options.
- **Return Value**: Configured Pipeline object ready for execution.
- **Errors**: Raises `ValueError` for invalid configurations and `RuntimeError` for system errors.

### Function: `run_pipeline(pipeline: Pipeline, environment: str = "development", **kwargs) -> Dict`

- **Description**: Execute a configured pipeline with full orchestration and monitoring.
- **Parameters**:
    - `pipeline`: Pipeline object to execute.
    - `environment`: Target deployment environment.
    - `**kwargs`: Execution-specific parameters (dry_run, timeout, etc.).
- **Return Value**:
    ```python
    {
        "status": "success|failed|cancelled",
        "pipeline_id": <str>,
        "execution_time": <float>,
        "stages_completed": <int>,
        "stages_total": <int>,
        "artifacts": [<list_of_artifacts>],
        "reports": {<execution_reports>}
    }
    ```
- **Errors**: Raises `PipelineExecutionError` for execution failures.

### Function: `manage_deployments(deployment_config: Dict, environment: str, **kwargs) -> Deployment`

- **Description**: Handle deployment orchestration with rollback capabilities.
- **Parameters**:
    - `deployment_config`: Deployment configuration including artifacts and targets.
    - `environment`: Target environment for deployment.
    - `**kwargs`: Deployment-specific options (strategy, timeout, etc.).
- **Return Value**: Deployment object with status tracking and management capabilities.
- **Errors**: Raises `DeploymentError` for deployment failures.

### Function: `monitor_pipeline_health(pipeline_id: str, **kwargs) -> Dict`

- **Description**: Real-time monitoring of pipeline execution health and metrics.
- **Parameters**:
    - `pipeline_id`: ID of pipeline to monitor.
    - `**kwargs`: Monitoring configuration options.
- **Return Value**:
    ```python
    {
        "pipeline_id": <str>,
        "status": "running|completed|failed",
        "health_score": <float>,
        "current_stage": <str>,
        "progress_percentage": <float>,
        "metrics": {<performance_metrics>},
        "alerts": [<list_of_alerts>]
    }
    ```
- **Errors**: Raises `MonitoringError` for monitoring system failures.

### Function: `generate_pipeline_reports(pipeline_id: str, report_types: List[str] = None, **kwargs) -> Dict`

- **Description**: Generate comprehensive pipeline execution reports and analytics.
- **Parameters**:
    - `pipeline_id`: ID of pipeline to report on.
    - `report_types`: Types of reports to generate (performance, quality, deployment).
    - `**kwargs`: Report generation options.
- **Return Value**:
    ```python
    {
        "pipeline_id": <str>,
        "reports": {
            "performance": {<performance_data>},
            "quality": {<quality_metrics>},
            "deployment": {<deployment_data>}
        },
        "generated_at": <timestamp>,
        "format": "json|html|pdf"
    }
    ```
- **Errors**: Raises `ReportGenerationError` for report creation failures.

### Function: `handle_rollback(deployment_id: str, strategy: str = "immediate", **kwargs) -> Dict`

- **Description**: Execute automated rollback for failed deployments.
- **Parameters**:
    - `deployment_id`: ID of deployment to rollback.
    - `strategy`: Rollback strategy (immediate, gradual, blue-green).
    - `**kwargs`: Rollback-specific configuration.
- **Return Value**:
    ```python
    {
        "rollback_id": <str>,
        "status": "initiated|completed|failed",
        "strategy": <str>,
        "execution_time": <float>,
        "rollback_steps": [<list_of_steps>],
        "verification_results": {<rollback_verification>}
    }
    ```
- **Errors**: Raises `RollbackError` for rollback execution failures.

### Function: `optimize_pipeline_performance(pipeline: Pipeline, metrics: Dict, **kwargs) -> Dict`

- **Description**: Analyze and optimize pipeline performance based on execution metrics.
- **Parameters**:
    - `pipeline`: Pipeline to optimize.
    - `metrics`: Performance metrics from previous executions.
    - `**kwargs`: Optimization configuration options.
- **Return Value**:
    ```python
    {
        "optimized_pipeline": <Pipeline>,
        "performance_improvements": {<improvement_metrics>},
        "recommendations": [<list_of_recommendations>],
        "estimated_gain": <float>
    }
    ```
- **Errors**: Raises `OptimizationError` for optimization analysis failures.

## Data Structures

### Pipeline
Represents a CI/CD pipeline configuration:
```python
{
    "id": <str>,
    "name": <str>,
    "stages": [<list_of_PipelineStage>],
    "config": {<pipeline_configuration>},
    "created_at": <timestamp>,
    "status": "draft|active|archived"
}
```

### PipelineStage
Represents an individual pipeline stage:
```python
{
    "name": <str>,
    "jobs": [<list_of_job_definitions>],
    "dependencies": [<list_of_upstream_stages>],
    "environment": <str>,
    "timeout": <int>,
    "retry_policy": {<retry_configuration>}
}
```

### Deployment
Represents a deployment configuration and status:
```python
{
    "id": <str>,
    "pipeline_id": <str>,
    "environment": <str>,
    "artifacts": [<list_of_artifacts>],
    "status": "pending|in_progress|completed|failed|rolled_back",
    "start_time": <timestamp>,
    "end_time": <timestamp>,
    "rollback_available": <bool>
}
```

### Environment
Represents a deployment environment:
```python
{
    "name": <str>,
    "type": "development|staging|production",
    "config": {<environment_specific_config>},
    "endpoints": [<list_of_service_endpoints>],
    "credentials": {<secure_credential_references>}
}
```

### PipelineReport
Represents comprehensive pipeline execution analytics:
```python
{
    "pipeline_id": <str>,
    "execution_id": <str>,
    "duration": <float>,
    "success_rate": <float>,
    "stage_metrics": {<per_stage_metrics>},
    "quality_metrics": {<code_quality_data>},
    "performance_metrics": {<execution_performance>},
    "generated_at": <timestamp>
}
```

### RollbackStrategy
Defines rollback execution strategy:
```python
{
    "type": "immediate|gradual|blue_green",
    "backup_retention": <int>,
    "verification_steps": [<list_of_verification_steps>],
    "timeout": <int>,
    "notification_channels": [<list_of_channels>]
}
```

## Error Handling

All functions follow consistent error handling patterns:

- **Configuration Errors**: `ValueError` for invalid parameters or configurations
- **Execution Errors**: `PipelineExecutionError`, `DeploymentError`, `RollbackError`
- **System Errors**: `RuntimeError` for underlying system failures
- **Monitoring Errors**: `MonitoringError` for health monitoring failures
- **Reporting Errors**: `ReportGenerationError` for report creation issues
- **Optimization Errors**: `OptimizationError` for performance analysis failures

## Integration Patterns

### With Build Synthesis
```python
from codomyrmex.deployment import create_build_target
from codomyrmex.ci_cd_automation import create_pipeline

# Create build target
build_target = create_build_target("my_app", source_path="src")

# Create pipeline with build stage
pipeline = create_pipeline("app_pipeline", [
    {"name": "build", "jobs": [build_target]},
    {"name": "test", "jobs": [...]},
    {"name": "deploy", "jobs": [...]}
])
```

### With Project Orchestration
```python
from codomyrmex.logistics.orchestration.project import execute_workflow
from codomyrmex.ci_cd_automation import run_pipeline

# Execute full CI/CD workflow
result = execute_workflow("ci_cd_pipeline", {
    "ci_cd_automation": {
        "pipeline_name": "production_deploy",
        "environment": "production",
        "quality_gates": True
    }
})
```

## Security Considerations

- **Credential Management**: Pipeline configurations may contain sensitive credentials
- **Access Control**: Pipeline execution should be restricted based on user permissions
- **Audit Logging**: All pipeline activities are logged for compliance and debugging
- **Secure Rollbacks**: Rollback operations maintain data integrity and security
- **Environment Isolation**: Deployments are isolated between environments for security

## Performance Characteristics

- **Scalability**: Supports concurrent pipeline execution
- **Resource Efficiency**: Intelligent resource allocation and cleanup
- **Monitoring Overhead**: Minimal performance impact from monitoring systems
- **Caching**: Build artifacts and test results are cached for efficiency
- **Parallelization**: Pipeline stages can execute in parallel when dependencies allow


## Navigation Links

- **Parent**: [Project Overview](../README.md)
- **Module Index**: [All Agents](../../AGENTS.md)
- **Documentation**: [Reference Guides](../../../docs/README.md)
- **Home**: [Root README](../../../README.md)
