Optimizing Agent Performance

Maximize efficiency

Learn proven techniques to optimize your agent systems for speed, accuracy, and cost-effectiveness.

Performance Fundamentals

Response Time

Minimize latency through efficient prompt design, model selection, and caching strategies.

Accuracy

Improve output quality through better training data, prompt engineering, and validation systems.

Cost Efficiency

Optimize API usage, model selection, and resource allocation to minimize operational costs.

Scalability

Design systems that maintain performance as workload increases through parallel processing and load balancing.

Prompt Optimization

Efficient Prompt Design

Best Practices:

Be Specific: Clear, detailed instructions reduce ambiguity and improve accuracy
Use Examples: Few-shot examples guide the model toward desired output format
Structure Information: Use bullet points, numbered lists, and clear sections
Set Constraints: Specify length limits, format requirements, and output structure

Prompt Templates:

# Role Definition
You are a [SPECIFIC_ROLE] with expertise in [DOMAIN].

# Task
[CLEAR_TASK_DESCRIPTION]

# Context
[RELEVANT_BACKGROUND_INFO]

# Format
Respond in the following format:
- Key Point 1: [explanation]
- Key Point 2: [explanation]
- Conclusion: [summary]

# Constraints
- Maximum 200 words
- Use professional tone
- Include specific examples

Model Selection Strategy

Model Comparison Matrix:

Use Case	Recommended Model	Speed	Cost	Quality
Simple Classification	GPT-3.5-turbo	Fast	Low	Good
Complex Analysis	GPT-4	Slow	High	Excellent
Creative Writing	Claude-3	Medium	Medium	Excellent
Code Generation	GPT-4	Slow	High	Excellent

Caching Strategies

Multi-Level Caching

Request → L1 Cache (Memory) → L2 Cache (Redis) → L3 Cache (Database) → API Call
           ↓ Hit (1ms)        ↓ Hit (10ms)       ↓ Hit (100ms)      ↓ Miss (2000ms)
         Return Result      Return Result      Return Result      Make API Call

Response Caching

Cache complete responses for identical queries. Implement cache invalidation based on content freshness requirements.

Partial Result Caching

Cache intermediate results from multi-step workflows to avoid recomputing common sub-tasks.

Memory Caching

Cache frequently accessed memories and search results to reduce database queries and embedding computations.

Parallel Processing

Workflow Parallelization

Parallelization Opportunities:

Independent Tasks: Run unrelated agents simultaneously
Data Processing: Split large datasets across multiple agents
Multi-Source Research: Query different sources in parallel
Validation: Run multiple validation checks concurrently

Implementation Example:

// Sequential (slow)
const result1 = await agent1.process(data)
const result2 = await agent2.process(data)
const result3 = await agent3.process(data)
// Total time: 6 seconds

// Parallel (fast)
const [result1, result2, result3] = await Promise.all([
  agent1.process(data),
  agent2.process(data),
  agent3.process(data)
])
// Total time: 2 seconds

Resource Management

Rate Limiting and Throttling

API Rate Limits

Implement exponential backoff for rate limit errors
Distribute requests across multiple API keys
Queue requests during high-traffic periods
Monitor usage to avoid hitting limits

Cost Management

Set daily/monthly spending limits
Use cheaper models for simple tasks
Implement request batching where possible
Monitor token usage and optimize prompts

Monitoring and Metrics

Key Performance Indicators

Speed Metrics

Average response time per agent
95th percentile latency
Cache hit rates
Queue wait times

Quality Metrics

Task completion accuracy
User satisfaction scores
Error rates by agent type
Retry/failure rates

Advanced Optimization Techniques

Dynamic Model Selection

Automatically choose the optimal model based on task complexity, urgency, and cost constraints.

function selectModel(task) {
  if (task.complexity === 'low' && task.urgency === 'high') {
    return 'gpt-3.5-turbo'  // Fast and cheap
  }
  if (task.complexity === 'high' && task.accuracy_required > 0.9) {
    return 'gpt-4'  // Slow but accurate
  }
  if (task.type === 'creative') {
    return 'claude-3'  // Best for creative tasks
  }
  return 'gpt-3.5-turbo'  // Default fallback
}

Adaptive Batching

Group similar requests together to reduce API calls and improve throughput.

Batch similar classification tasks
Group content generation requests
Combine memory searches with similar queries
Process multiple validation checks together

Performance Testing

Load Testing

Test your system under various load conditions to identify bottlenecks and scaling limits.

A/B Testing

Compare different prompt versions, model selections, and workflow configurations to optimize performance.

Stress Testing

Push your system beyond normal operating conditions to understand failure modes and recovery behavior.