Performance Optimization Guide

This comprehensive guide covers strategies and techniques for optimizing agent performance, including caching, parallel execution, streaming, resource management, and monitoring.

Table of Contents

1. Overview

2. Caching Strategies

3. Parallel Execution

4. Streaming Optimization

5. Resource Optimization

6. Memory Optimization

7. Monitoring and Profiling

8. Best Practices

Overview

Performance optimization techniques:

• Tool Caching: 30-50% cost reduction, faster responses

• Parallel Execution: 3-5x performance improvement

• Streaming: Better UX, progressive results

• Resource Management: Prevent overload, ensure stability

• Memory Optimization: Reduce memory usage

• Monitoring: Identify bottlenecks

Caching Strategies

Pattern 1: Aggressive Caching

Cache expensive operations aggressively.

from aiecs.domain.agent import CacheConfig

cache_config = CacheConfig(
    enabled=True,
    default_ttl=3600,  # 1 hour default
    tool_specific_ttl={
        "search": 7200,  # 2 hours for search
        "translation": 86400,  # 24 hours for translation
        "calculator": 0  # Don't cache calculator
    },
    max_cache_size=5000
)

agent = HybridAgent(
    agent_id="agent-1",
    llm_client=llm_client,
    tools=["search", "translation", "calculator"],
    config=config,
    cache_config=cache_config
)

Pattern 2: Selective Caching

Cache only expensive operations.

cache_config = CacheConfig(
    enabled=True,
    default_ttl=0,  # Don't cache by default
    tool_specific_ttl={
        "expensive_api": 3600,  # Cache expensive API
        "slow_operation": 1800  # Cache slow operations
    }
)

Pattern 3: Cache Invalidation

Invalidate cache when data changes.

# Invalidate cache after data update
agent.invalidate_cache(tool_name="search")

# Invalidate by pattern
agent.invalidate_cache(pattern="query:Python*")

Parallel Execution

Pattern 1: Maximize Parallelism

Execute maximum independent tools in parallel.

# Execute many tools in parallel
tool_calls = [
    {"tool_name": "search", "parameters": {"query": "Python"}},
    {"tool_name": "weather", "parameters": {"location": "NYC"}},
    {"tool_name": "calculator", "parameters": {"operation": "add", "a": 1, "b": 2}},
    {"tool_name": "translator", "parameters": {"text": "Hello", "target": "es"}}
]

results = await agent.execute_tools_parallel(
    tool_calls,
    max_concurrency=10  # High concurrency
)

Pattern 2: Batch Processing

Process tasks in batches for better throughput.

# Process tasks in batches
tasks = [task1, task2, task3, ...]

batch_size = 10
for i in range(0, len(tasks), batch_size):
    batch = tasks[i:i+batch_size]
    results = await asyncio.gather(*[
        agent.execute_task(task, context) for task in batch
    ])

Streaming Optimization

Pattern 1: Progressive Display

Use streaming for better UX.

# Stream results progressively
async for event in agent.execute_task_streaming(task, context):
    if event['type'] == 'token':
        # Display tokens as they arrive
        display_token(event['content'])
    elif event['type'] == 'tool_result':
        # Display tool results immediately
        display_result(event['result'])

Pattern 2: Buffer Optimization

Optimize buffer size for smooth streaming.

# Buffer tokens for smooth display
buffer = []
buffer_size = 20

async for event in agent.execute_task_streaming(task, context):
    if event['type'] == 'token':
        buffer.append(event['content'])
        if len(buffer) >= buffer_size:
            display(''.join(buffer))
            buffer.clear()

Resource Optimization

Pattern 1: Optimal Rate Limits

Set rate limits based on API constraints.

from aiecs.domain.agent.models import ResourceLimits

# Match API rate limits
resource_limits = ResourceLimits(
    max_tokens_per_minute=60000,  # Match API limit
    max_tool_calls_per_minute=500,  # Match tool API limit
    token_burst_size=120000  # Allow 2x burst
)

Pattern 2: Concurrent Task Optimization

Optimize concurrent tasks based on resources.

import os

# Set based on CPU cores
cpu_count = os.cpu_count() or 4
max_concurrent = cpu_count * 2  # 2x CPU cores

resource_limits = ResourceLimits(
    max_concurrent_tasks=max_concurrent
)

Memory Optimization

Pattern 1: Conversation Compression

Use compression to reduce memory usage.

from aiecs.domain.context import CompressionConfig

compression_config = CompressionConfig(
    strategy="summarize",
    auto_compress_enabled=True,
    auto_compress_threshold=50,
    auto_compress_target=30
)

context_engine = ContextEngine(compression_config=compression_config)

Pattern 2: Cache Size Limits

Limit cache size to control memory.

cache_config = CacheConfig(
    enabled=True,
    max_cache_size=1000,  # Limit cache entries
    max_memory_mb=100  # Limit cache memory
)

Monitoring and Profiling

Pattern 1: Performance Profiling

Profile agent performance.

import time

# Profile execution time
start = time.time()
result = await agent.execute_task(task, context)
duration = time.time() - start

print(f"Execution time: {duration:.2f}s")

# Profile specific operations
with agent.track_operation_time("data_processing"):
    result = await agent.execute_task(task, context)

Pattern 2: Metrics Analysis

Analyze performance metrics.

# Get performance metrics
metrics = agent.get_performance_metrics()

print(f"Average response time: {metrics['avg_response_time']}s")
print(f"P95 response time: {metrics['p95_response_time']}s")
print(f"P99 response time: {metrics['p99_response_time']}s")

# Identify bottlenecks
if metrics['p95_response_time'] > 3.0:
    logger.warning("P95 response time exceeds threshold")

Pattern 3: Cache Performance

Monitor cache performance.

stats = agent.get_cache_stats()

print(f"Cache hit rate: {stats['hit_rate']:.1%}")
print(f"Cache size: {stats['size']}")

if stats['hit_rate'] < 0.3:
    logger.warning("Low cache hit rate - consider adjusting TTL")

Best Practices

1. Combine Optimization Techniques

Combine multiple optimization techniques:

# Optimized agent configuration
cache_config = CacheConfig(
    enabled=True,
    default_ttl=300,
    tool_specific_ttl={"search": 600}
)

resource_limits = ResourceLimits(
    max_concurrent_tasks=10,
    max_tokens_per_minute=50000
)

compression_config = CompressionConfig(
    auto_compress_enabled=True,
    auto_compress_threshold=50
)

agent = HybridAgent(
    agent_id="agent-1",
    llm_client=llm_client,
    tools=["search"],
    config=config,
    cache_config=cache_config,
    resource_limits=resource_limits,
    context_engine=ContextEngine(compression_config=compression_config),
    enable_parallel_execution=True,
    enable_streaming=True
)

2. Monitor and Adjust

Continuously monitor and adjust:

# Monitor performance
metrics = agent.get_performance_metrics()
cache_stats = agent.get_cache_stats()

# Adjust based on metrics
if metrics['avg_response_time'] > 2.0:
    # Increase caching
    cache_config.default_ttl = 600
    
if cache_stats['hit_rate'] < 0.3:
    # Adjust cache TTL
    cache_config.default_ttl = 1800

3. Profile Before Optimizing

Profile to identify bottlenecks:

# Profile before optimizing
with agent.track_operation_time("full_execution"):
    result = await agent.execute_task(task, context)

# Get operation metrics
operation_metrics = agent.get_operation_metrics("full_execution")
print(f"Operation time: {operation_metrics['avg_time']}s")

# Optimize based on profiling results

Summary

Performance optimization provides:

• ✅ 30-50% cost reduction (caching)

• ✅ 3-5x speed improvement (parallel execution)

• ✅ Better UX (streaming)

• ✅ Resource stability (rate limiting)

• ✅ Memory efficiency (compression)

Key Optimization Techniques:

• Cache expensive operations

• Execute tools in parallel

• Stream for better UX

• Set appropriate rate limits

• Compress conversations

• Monitor and adjust

For more details, see:

• Tool Caching

• Parallel Tool Execution

• Streaming

• Resource Management