Agent Best Practices

Proven guidelines for building effective, maintainable, and efficient AI agents.

✨ Following Best Practices Results In

40%
Faster Development
60%
Fewer Errors
35%
Token Savings
95%
Task Success Rate

🎯 Design Principles

Core principles for effective agent design.

Single Responsibility

Each agent should excel at one specific domain

Do

  • • Focus on one specialty
  • • Master specific tools
  • • Deep domain knowledge

Don't

  • • Jack of all trades
  • • Overlapping responsibilities
  • • Generic capabilities

Clear Communication

Agents should provide clear, actionable outputs

Do

  • • Structured responses
  • • Progress updates
  • • Error explanations

Don't

  • • Vague messages
  • • Silent failures
  • • Ambiguous results

Efficient Resource Usage

Optimize token usage and processing time

Do

  • • Context sharing
  • • Incremental updates
  • • Smart caching

Don't

  • • Redundant processing
  • • Full context resends
  • • Unnecessary API calls

Robust Error Handling

Gracefully handle failures and edge cases

Do

  • • Validate inputs
  • • Retry mechanisms
  • • Fallback strategies

Don't

  • • Crash on errors
  • • Ignore failures
  • • Assume success

📋 Implementation Guidelines

Step-by-step guidelines for agent implementation.

Agent Definition

Critical
  • Start with a clear role statement
  • List specific capabilities and limitations
  • Define success metrics and KPIs
  • Include example use cases

Tool Selection

High
  • Choose minimal required MCPs
  • Verify tool compatibility
  • Test tool performance
  • Document tool dependencies

Testing Strategy

High
  • Create comprehensive test scenarios
  • Include edge case testing
  • Validate output quality
  • Monitor performance metrics

Documentation

Medium
  • Document all capabilities
  • Provide usage examples
  • Maintain changelog
  • Include troubleshooting guide

💻 Example Implementation

Well-Structured Agent Definition

typescript
class WellDesignedAgent extends BaseAgent {
  constructor() {
    super({
      name: 'performance-optimizer',
      specialty: 'Performance Optimization',
      version: '1.0.0'
    });
  }
  
  // Single responsibility: performance only
  async analyzePerformance(code: string): Promise<Analysis> {
    // Validate input
    if (!this.validateInput(code)) {
      throw new ValidationError('Invalid code input');
    }
    
    // Use shared context to reduce tokens
    const context = await this.getSharedContext();
    
    // Process with error handling
    try {
      const result = await this.processWithRetry(code, context);
      
      // Validate output quality
      if (!this.validateOutput(result)) {
        return this.fallbackStrategy();
      }
      
      return result;
    } catch (error) {
      this.logger.error('Analysis failed', error);
      return this.handleError(error);
    }
  }
}

⚠️ Common Pitfalls

Avoid these common mistakes when building agents.

Over-Engineering

High Impact

Making agents too complex or feature-rich

Solution: Start simple, iterate based on needs

Poor Context Management

High Impact

Not sharing context between agents effectively

Solution: Implement shared context pools

Lack of Validation

Medium Impact

Not validating agent outputs and quality

Solution: Add validation layers and quality checks

Ignoring Performance

Medium Impact

Not optimizing for speed and efficiency

Solution: Profile and optimize critical paths

Tips for Success

Development Tips

  • • Start with a minimal viable agent
  • • Test with real-world scenarios
  • • Monitor performance metrics
  • • Iterate based on feedback

Maintenance Tips

  • • Keep agents updated with latest tools
  • • Regular performance audits
  • • Document all changes
  • • Version control agent definitions