How to Optimize Latency in Voice Agents

Latency, the time between a user's command and the system's response, is arguably the most critical factor in voice user experience. Yet despite its importance, latency remains widely misunderstood by teams building voice applications. For instance, many teams understand the importance of latency and track latency averages, however average latency metrics hide critical failures. Without monitoring p45, p90, and p99 latencies across each pipeline stage—STT, LLM, TTS—optimization becomes guesswork.

In order to optimize latency for voice agents, it's critical to measure and track the right latency metrics and use a voice observability tool to track latency at each pipeline stage.

Why Average Latency Metrics Fail

Averages obscure latency distribution metrics. For example, a 300ms average latency may appear successful in metrics reviews but hide the fact that 10% of calls spike to 1500ms. From a QA perspective, these averages suggest performance stability, while in practice the long-tail latencies create severe user experience degradation. Relying solely on averages risks overlooking these outliers, giving teams a false sense of confidence about system performance.

Latency compounds through pipelines. A STT step may take an extra 200ms, an LLM request may queue before execution and TTS rendering may introduce further delays. late. Individually, these increments appear minor, but they cascade into multi-second pauses that disrupt the user experience. If teams rely on average latency alone, these compounding effects remain hidden.

Understanding P45, P90, and P99

Percentiles reveal experience distribution across users and not averages.

P45 represents the optimal conditions of voice agent performance at peak. When p45 exceeds ~200ms,it often points to deeper architectural inefficiencies that need addressing.

P90 shows what 1 in 10 users experience. In a 10-minute conversation with 20 turns, users encounter p90 latency twice. These delays are noticeable and can negatively impact the user experience.

P99 exposes the extreme tail of voice agent performance, exposing failure modes that drive complaints and abandonments. Spikes here often reveal infrastructure limits such as cold starts, network congestion, model queue depths, memory pressure.

Building Production Monitoring Systems

Effective latency management requires continuous observability and measurement infrastructure.

Essential metrics by call and component:

• Time to first word: call connection to agent speech
• Turn-level latency: each exchange, not call averages
• Component timing: STT duration, LLM processing, TTS generation
• Queue depths: predictive indicators of future delays

Percentile tracking configuration:

• Bucketed histograms replace simple averages
• Separate metrics by component and operation type
• Tag with context: device type, audio quality, model version, geographic region

Histogram buckets:

• 50ms buckets: 0-500ms (conversation threshold)
• 100ms buckets: 500-1000ms (degradation zone)
• 250ms buckets: 1000-3000ms (abandonment risk)
• Single bucket: >3000ms (complete failure)

Alert thresholds based on user impact:

• p90 > 500ms: noticeable degradation
• p99 > 1000ms: user frustration
• Any percentile > 2000ms: abandonment risk

Converting Metrics to Improvements

Monitoring identifies and pinpoints the specific issues, but optimization requires action to solves these issues.

The optimization cycle:

1. Monitor percentiles across all stages
2. Identify the component causing spikes
3. Apply targeted fixes to that component
4. Verify improvement in production
5. Watch for regression or new bottlenecks
6. Return to monitoring

Prioritize by user impact, not technical severity. P99 spikes affecting greetings matter more than p50 delays in rarely-used functions.

A/B testing validates provider decisions:

• Route 10% of traffic through new STT providers
• Compare p90 and p99 latencies, not averages
• Calculate cost per millisecond saved
• Make data-driven switches

Regression detection prevents backsliding. Every model update, deployment, or configuration change triggers automatic latency comparison. A 50ms p99 increase compounds through pipelines.

Hamming's approach monitors three dimensions: what is said, how it's said, and what's done. Comprehensive monitoring ensures latency improvements maintain accuracy and user experience.

Engineering Sub-Second Response Times

Consistent sub-second response requires architectural optimization beyond basic improvements.

Regional deployment eliminates foundational latency. US-East hosting for US-West users adds 60-80ms round trip, multiplied by every service hop. Geographic distribution is mandatory for production voice agents.

Prewarmed instances prevent cold starts. First calls shouldn't wait 2-3 seconds for model loading. Maintain warm instances with synthetic health checks.

Connection pooling saves 20-50ms per request. Persistent pools eliminate HTTPS connection overhead. Connection delays compound faster than other latency sources.

Protocol selection impacts resilience. WebRTC beats WebSockets by 50% in lossy networks. The protocol handles packet loss gracefully, maintaining quality during congestion.

Pipeline parallelization breaks sequential bottlenecks:

• Start TTS rendering before LLM completion for predictable endings
• Prepare common responses while processing unique requests
• Launch speculative execution for frequent paths

Intelligent caching extends beyond static responses:

• Cache partial LLM outputs for similar queries
• Store preprocessed audio for common phrases
• Remember user preferences to skip discovery delays

Provider redundancy ensures consistency:

• Route to backup providers when primary latency spikes
• Maintain hot standbys for critical components
• Switch automatically based on real-time p99 monitoring

Managing Edge Cases

Edge cases destroy p99 latency, even optimized pipelines can still produce multi-second delays.

Common edge cases:

• Background noise forcing expensive STT processing modes
• Heavy accents invoking fallback models
• Cross-talk requiring speaker separation
• Elderly users with TV interference triggering repeated recognition

Each requires specific handling. Implement graceful degradation over perfect processing. Fast approximation beats slow precision for voice interaction. Quick "Could you repeat that?" beats two-second silence.

Testing with diverse voice profiles reveals hidden edge cases. Use synthetic voice characters with various accents, speech patterns, and background conditions. Monitor their p99 separately. Optimize for problem segments.

WebRTC achieves sub-100ms latency in perfect conditions. Production users don't provide perfect conditions.

Establishing Continuous Optimization

Latency optimization requires ongoing monitoring. Every change affects percentiles.

The optimization lifecycle:

1. Baseline: Establish current p45/p90/p99 across all stages
2. Identify: Find components causing p99 spikes
3. Optimize: Apply targeted fixes
4. Verify: Confirm percentile improvements
5. Monitor: Watch for regression
6. Iterate: Return to identification

Warning signs requiring immediate action:

• User complaints despite "good" averages—check p99
• Performance gaps between testing and production—monitor real calls
• Model updates causing slowdowns—compare percentile trends
• Intermittent slowness—analyze percentile distributions

This post is the first in a three-part series on optimizing latency in voice agents. The next blog post will walk through how to run a latency audit. The third part will dive deeper into advanced optimization strategies for managing regressions and edge cases in production.

Hamming provides stage-by-stage latency breakdown purpose-built for voice agents. Track percentiles at every pipeline stage. Compare performance across provider changes. Catch regressions before users notice. Transform production issues into test cases.

Voice agents have milliseconds to maintain natural conversation. Average metrics fail when p99 breaks that expectation. Start with a latency audit. Implement percentile monitoring today.

Ready to see where latency lives in your voice agent pipeline? Explore how Hamming meausures and monitors performance bottlenecks.