Overview

Best practices and guidelines for building production-ready BroxiAI applications

Learn industry best practices for building robust, scalable, and maintainable AI applications with BroxiAI.

Design Principles

Workflow Design

Keep Workflows Simple

Start with minimal viable workflows
Break complex processes into smaller flows
Use clear, descriptive component names
Document your workflow logic

Modular Architecture

Create reusable components
Separate concerns (data processing, AI logic, output)
Use consistent patterns across workflows
Plan for extensibility

Error Handling

Anticipate and handle edge cases
Implement graceful fallbacks
Provide meaningful error messages
Log errors for debugging

Component Organization

Naming Conventions

# Good naming examples
customer_support_agent
document_processor_v2
email_notification_sender

# Avoid generic names
agent1
component
my_flow

Component Configuration

Use environment variables for sensitive data
Document component parameters
Set reasonable defaults
Validate inputs

Performance Optimization

Memory Management

Conversation Memory

Set appropriate memory limits
Clear unnecessary conversation history
Use summarization for long conversations
Monitor memory usage

Data Processing

Chunk large documents appropriately
Optimize embedding dimensions
Use efficient data structures
Implement caching strategies

API Optimization

Request Efficiency

Batch similar operations
Use streaming for long responses
Implement proper timeouts
Handle rate limiting gracefully

Model Selection

Choose appropriate model sizes
Use task-specific models
Consider latency vs. quality trade-offs
Monitor token usage and costs

Caching Strategies

Component-Level Caching

{
  "cache_config": {
    "enabled": true,
    "ttl": 3600,
    "key_strategy": "input_hash",
    "storage": "memory"
  }
}

Vector Database Caching

Cache embedding calculations
Store frequently accessed results
Use semantic caching for similar queries
Implement cache invalidation strategies

Security Best Practices

Data Protection

Sensitive Data Handling

Never hardcode API keys in workflows
Use BroxiAI's secure variable storage
Implement data encryption
Follow data retention policies

Access Control

Use role-based permissions
Implement least privilege principle
Audit access logs regularly
Secure API endpoints

API Security

Authentication

# Good: Use environment variables
client = BroxiClient(api_token=os.environ['BROXI_API_TOKEN'])

# Bad: Hardcoded tokens
client = BroxiClient(api_token="broxi-1234567890abcdef")

Input Validation

Sanitize user inputs
Validate data formats
Implement input size limits
Prevent injection attacks

Scalability Guidelines

Architecture Patterns

Microflows Pattern

Break large workflows into smaller, focused flows
Enable independent scaling
Improve maintainability
Facilitate testing

Event-Driven Architecture

Use webhooks for asynchronous processing
Implement event sourcing where appropriate
Design for eventual consistency
Handle out-of-order events

Resource Management

Concurrency Control

Design for concurrent execution
Avoid shared state
Use stateless components
Implement proper locking

Load Distribution

Plan for traffic spikes
Implement circuit breakers
Use graceful degradation
Monitor system health

Development Workflow

Version Control

Flow Versioning

Use semantic versioning
Maintain backward compatibility
Document breaking changes
Test upgrades thoroughly

Configuration Management

# Environment-specific configs
development:
  debug: true
  log_level: debug
  rate_limit: 10

production:
  debug: false
  log_level: info
  rate_limit: 100

Testing Strategy

Unit Testing

Test individual components
Mock external dependencies
Validate input/output formats
Test error conditions

Integration Testing

Test complete workflows
Validate API integrations
Test with realistic data
Performance testing

User Acceptance Testing

Test with real users
Validate business requirements
Test edge cases
Gather feedback

Deployment Process

Staging Environment

Mirror production configuration
Test with production-like data
Validate performance metrics
Test rollback procedures

Blue-Green Deployment

Maintain two identical environments
Switch traffic gradually
Monitor for issues
Quick rollback capability

Monitoring and Observability

Key Metrics

Performance Metrics

Response time (p50, p95, p99)
Throughput (requests per second)
Error rate and types
Resource utilization

Business Metrics

User engagement
Conversion rates
Feature adoption
Cost per transaction

Logging Strategy

Structured Logging

{
  "timestamp": "2024-01-15T10:30:00Z",
  "level": "info",
  "workflow_id": "customer_support",
  "user_id": "user_123",
  "action": "message_processed",
  "duration": 1.2,
  "tokens_used": 150
}

Log Levels

ERROR: System failures, exceptions
WARN: Performance issues, degraded service
INFO: Normal operations, business events
DEBUG: Detailed diagnostic information

Alerting

Alert Categories

Critical: Service down, security breach
Warning: Performance degradation, high error rate
Info: Deployment notifications, scheduled events

Alert Best Practices

Define clear escalation procedures
Avoid alert fatigue
Include actionable information
Test alert mechanisms

Cost Optimization

Resource Usage

Model Optimization

Choose cost-effective models
Optimize prompt lengths
Use caching to reduce API calls
Monitor token usage patterns

Infrastructure Efficiency

Right-size resources
Use spot instances where appropriate
Implement auto-scaling
Regular cost reviews

Budget Management

Cost Tracking

cost_tracking:
  budget_alerts:
    - threshold: 80%
      action: notify_team
    - threshold: 95% 
      action: throttle_requests
  attribution:
    - dimension: project
    - dimension: environment
    - dimension: user_group

Quality Assurance

Code Quality

Code Review Process

Peer review all workflow changes
Use consistent coding standards
Document complex logic
Maintain code quality metrics

Automated Quality Checks

Lint workflow configurations
Validate component connections
Check for security issues
Performance regression testing

Documentation Standards

Workflow Documentation

Purpose and use case
Input/output specifications
Configuration requirements
Troubleshooting guide

Component Documentation

Functionality description
Parameter specifications
Usage examples
Best practices

Maintenance and Updates

Regular Maintenance

Health Checks

Monitor workflow performance
Check for deprecated components
Update dependencies
Review security patches

Optimization Reviews

Analyze performance metrics
Identify bottlenecks
Optimize resource usage
Update configurations

Update Strategy

Dependency Management

Keep components up to date
Test updates in staging
Plan for breaking changes
Maintain compatibility matrix

Feature Adoption

Evaluate new BroxiAI features
Plan gradual rollouts
Monitor impact on existing workflows
Gather user feedback

Team Collaboration

Team Standards

Consistent naming conventions
Shared component libraries
Documentation requirements
Code review processes

Knowledge Sharing

Regular team meetings
Best practice documentation
Training sessions
Peer programming

Project Organization

Folder Structure

Production/
├── Customer Support/
│   ├── Main Agent
│   ├── Escalation Handler
│   └── Knowledge Base
├── Sales/
│   ├── Lead Qualifier
│   └── Follow-up Agent
└── Internal Tools/
    ├── Document Processor
    └── Report Generator

Troubleshooting Guidelines

Common Issues

Performance Problems

High latency: Check component configurations
Memory issues: Review conversation history
API errors: Validate credentials and rate limits
Timeout errors: Adjust timeout settings

Integration Issues

Connection failures: Check network and credentials
Data format errors: Validate input/output schemas
Authentication problems: Verify API keys and permissions
Version conflicts: Check compatibility requirements

Debugging Strategies

Systematic Approach

Reproduce the issue
Check logs and metrics
Isolate the problem component
Test with minimal configuration
Implement and verify fix

Tools and Techniques

Use playground for testing
Enable debug logging
Monitor real-time metrics
Test with sample data

Getting Help

Resources

Documentation: Comprehensive guides and references
Community Forums: Peer support and discussions
Support Team: Technical assistance
Training Materials: Video tutorials and workshops

Contributing

Share best practices with the community
Contribute to documentation
Report issues and suggestions
Participate in feature discussions

These best practices are continuously updated based on community feedback and platform evolution. Check back regularly for the latest recommendations.

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Last updated 11 days ago