# Cognition Enterprise Flows

This directory contains enterprise-grade flows for automating complex business processes using AI-powered crews.

## Cloud Resource Management Flow

### Business Case
The Cloud Resource Management Flow automates and secures cloud infrastructure provisioning while ensuring compliance and risk management.

### Process Flow
```mermaid
flowchart TD
    A[Resource Request] --> B[Risk Analysis]
    B --> C{Risk Level}
    C -->|High Risk > 0.7| D[Manager Approval]
    C -->|Medium Risk > 0.4| E[Auto-Provision with Checks]
    C -->|Low Risk| F[Auto-Provision]
    D -->|Approved| E
    D -->|Rejected| G[Request Denied]
    E --> H[Validation]
    F --> H
    H -->|Pass| I[Resource Active]
    H -->|Fail| J[Rollback]
```

### Key Features
- Risk-based approval routing
- Automated compliance checks
- Multi-level validation
- Audit trail generation
- Notification system integration

## Code Review Automation Flow

### Business Case
The Code Review Flow automates comprehensive code analysis and review processes, ensuring code quality, security, and maintainability.

### Process Flow
```mermaid
flowchart TD
    A[PR Created] --> B[Static Analysis]
    B --> C[Security Scan]
    C --> D[Pattern Analysis]
    D --> E[Review Generation]
    E --> F{Auto-Approve?}
    F -->|Yes| G[Merge PR]
    F -->|No| H[Request Changes]
    
    subgraph "Analysis Steps"
    B --> B1[Quality Check]
    B --> B2[Style Check]
    C --> C1[Vulnerability Scan]
    C --> C2[Dependency Check]
    D --> D1[Architecture Review]
    D --> D2[Pattern Detection]
    end
```

### Key Features
- Comprehensive code analysis
- Security vulnerability detection
- Pattern recognition
- Automated review comments
- Integration with GitHub/GitLab
- Custom approval conditions

## Technical Integration

### Flow Configuration
```mermaid
graph LR
    A[YAML Config] --> B[Flow Instance]
    B --> C[Crew Management]
    C --> D[Agent Execution]
    D --> E[State Management]
    E --> F[Persistence]
```

### Common Components
- State management using Pydantic models
- Event-driven execution flow
- Configurable notification systems
- Metrics and monitoring
- Audit logging

## Usage

1. Configure flow settings in `config/flows/`
2. Initialize flow through Cognition interface:
```python
from cognition import Cognition

cognition = Cognition()
result = cognition.code_review_flow().kickoff(
    inputs={
        "pr_number": 123,
        "repo": "org/repo"
    }
)
```

## Monitoring & Metrics

Both flows provide:
- Execution time tracking
- Success/failure rates
- Resource utilization metrics
- Audit trails
- Integration events

## Security & Compliance

- Role-based access control
- Audit logging
- Compliance checks
- Security scanning
- Approval workflows 