Performance Optimization Guide

Latency and Throughput for Agentic Systems

Document ID: C4-PERFORMANCE | Version: 1.0 | Category: P3 - Technical Deep Dives

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Executive Summary

Agentic systems face unique performance challenges: LLM latency, tool call overhead, and multi-agent coordination. This guide provides optimization patterns for production performance.

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Performance Bottlenecks

Latency Breakdown

Component	Typical Latency	Optimization Potential
LLM inference	2-30s	Model selection, caching
Tool execution	0.1-10s	Parallelization
Memory retrieval	0.05-0.5s	Index optimization
Agent coordination	1-5s	Protocol optimization
Network	0.05-0.2s	Edge deployment

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Optimization Patterns

Pattern 1: Response Streaming

async def stream_response(self, prompt: str):
    """Stream response for perceived latency reduction."""
    
    async for chunk in self.llm.stream(prompt):
        yield chunk
        
        # Early tool detection
        if self._detect_tool_call(chunk):
            # Start tool execution in parallel
            asyncio.create_task(self._prefetch_tool_result(chunk))

Pattern 2: Parallel Tool Execution

async def execute_tools_parallel(self, tool_calls: list):
    """Execute independent tools in parallel."""
    
    # Group by dependencies
    independent = [t for t in tool_calls if not t.dependencies]
    dependent = [t for t in tool_calls if t.dependencies]
    
    # Execute independent tools in parallel
    results = await asyncio.gather(*[
        self.execute_tool(t) for t in independent
    ])
    
    # Execute dependent tools sequentially
    for tool in dependent:
        results.append(await self.execute_tool(tool))
    
    return results

Pattern 3: Semantic Caching

class SemanticCache:
    """Cache by semantic similarity."""
    
    def __init__(self, similarity_threshold: float = 0.95):
        self.threshold = similarity_threshold
        self.cache = {}
    
    async def get(self, query: str) -> Optional[str]:
        query_embedding = await self.embed(query)
        
        for cached_query, (embedding, response) in self.cache.items():
            similarity = cosine_similarity(query_embedding, embedding)
            if similarity >= self.threshold:
                return response
        
        return None

Pattern 4: Speculative Execution

async def speculative_plan(self, task: str):
    """Speculatively execute likely next steps."""
    
    # Generate plan
    plan = await self.plan(task)
    
    # Start first step
    step1_task = asyncio.create_task(self.execute_step(plan.steps[0]))
    
    # Speculatively prepare step 2
    if len(plan.steps) > 1:
        step2_prep = asyncio.create_task(
            self.prepare_step(plan.steps[1])
        )
    
    step1_result = await step1_task
    
    # Use speculative prep if valid
    if step2_prep and self._is_valid_speculation(step1_result, plan.steps[1]):
        # Already prepared
        pass

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Scaling Strategies

Horizontal Scaling

Component	Scaling Strategy	Considerations
Agent workers	Stateless pods	Session affinity
Memory store	Sharded vectors	Query routing
Tool services	Load balanced	Idempotency
LLM calls	Request pooling	Rate limits

Vertical Optimization

Optimization	Technique	Impact
Context compression	Summarization	-30% tokens
Embedding cache	Redis/Memcached	-50% latency
Connection pooling	Persistent conns	-20% overhead
Batch processing	Request batching	+3x throughput

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Benchmarking

Key Metrics

Metric	Target	Measurement
P50 latency	<5s	Response time
P95 latency	<15s	Tail latency
P99 latency	<30s	Worst case
Throughput	>100 req/min	Requests/second
Token efficiency	<10K/request	Average tokens

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Quick Reference

Bottleneck	Optimization	Expected Gain
LLM latency	Streaming	50% perceived
Tool calls	Parallelization	40% reduction
Memory	Caching	60% hit rate
Coordination	Protocol opt	30% reduction

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Document maintained by CODITECT Performance Team