Integration Patterns and Best Practices

MCP Integration

All major frameworks support MCP for standardized tool integration:

Design Best Practices

Agent Design

Key principles for designing effective agents [33]:

1. Clear Boundaries: Separate decision-making, tools, and tasks
2. Structured Reasoning Loops: "Plan, then act" approach
3. Observability: Track every decision, tool call, and state change
4. Graceful Degradation: Handle failures without complete breakdown
5. Testability: Design for easy testing and evaluation

Multi-Agent Systems

Best practices for building multi-agent systems [34]:

1. Clear Roles: Each agent has defined responsibilities
2. Local Memory: Keep agent memory local to prevent conflicts
3. Explicit Communication: Define clear inter-agent protocols
4. Coordination Patterns: Choose appropriate orchestration strategy
5. Error Isolation: Failures in one agent shouldn't cascade

Production Considerations

Security Best Practices

Input Validation

• Validate all user inputs before processing
• Sanitize inputs to prevent injection attacks
• Implement rate limiting to prevent abuse
• Use schema validation for structured inputs

Tool Execution

• Run tools in sandboxed environments
• Limit available commands and permissions
• Set resource limits (CPU, memory, time)
• Log all tool executions for audit

Data Protection

• Encrypt sensitive data at rest and in transit
• Implement access controls for memory systems
• Anonymize or redact PII in logs
• Follow data retention policies

Observability and Tracing

What to Track

Tracing Tools

• LangSmith: Native LangChain/LangGraph tracing
• OpenTelemetry: Standard observability framework
• Weights & Biases: ML experiment tracking
• Custom Solutions: Build with your existing stack

Error Handling Patterns

Retry Strategies

import time
from functools import wraps

def retry_with_backoff(max_retries=3, base_delay=1):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(max_retries):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if attempt == max_retries - 1:
                        raise
                    delay = base_delay * (2 ** attempt)
                    time.sleep(delay)
            return None
        return wrapper
    return decorator

@retry_with_backoff(max_retries=3)
def call_llm(prompt):
    # LLM call that might fail
    pass

Fallback Strategies

• Model Fallback: Try alternative models on failure
• Tool Fallback: Use alternative tools for same task
• Graceful Degradation: Provide partial results
• Human Escalation: Route to human when stuck

Cost Optimization

Token Management

• Use smaller models for simple tasks
• Implement context compression
• Cache common responses
• Batch similar requests

Monitoring

class TokenTracker:
    def __init__(self):
        self.total_tokens = 0
        self.cost_per_token = 0.00001
    
    def track(self, input_tokens, output_tokens):
        self.total_tokens += input_tokens + output_tokens
        return self.total_tokens * self.cost_per_token
    
    def get_cost(self):
        return self.total_tokens * self.cost_per_token

Testing Strategies

Unit Testing

• Test individual tools in isolation
• Mock LLM responses for deterministic tests
• Validate input/output schemas

Integration Testing

• Test tool chains end-to-end
• Verify memory persistence
• Test error handling paths

Evaluation

• Create benchmark datasets
• Measure task completion rates
• Track quality metrics over time
• A/B test different configurations

Deployment Patterns

Architecture Options

References

1. CrewAI MCP Integration
2. OpenAI Agents SDK - MCP
3. Hatchworks - AI Agent Design Best Practices
4. Multi-Agent Systems Best Practices