Latency measures response speed, not accuracy. An agent can produce a perfectly correct answer but still fail a use case if it takes too long to arrive: a voice AI agent with several seconds of dead air before responding creates an awkward, unnatural phone call, regardless of how good the eventual answer is.

Latency compounds in multi-step agentic workflows: if a task requires five sequential model calls, each with meaningful latency, the total delay stacks up fast, which is part of why efficient orchestration and model-size routing (using a smaller, faster model for simple sub-steps) matters for real-time responsiveness.

Frequently Asked Questions

Why does latency matter more for voice AI than for text-based chat?

A delay of even one or two seconds feels natural in a chat window, but the same delay in a live phone conversation reads as an awkward, unnatural pause, since spoken conversation has much tighter real-time expectations than typed exchange.

What's the difference between latency and throughput?

Latency is how long a single request takes to get a response. Throughput is how many requests a system can handle in parallel over time. A system can have low latency for one request but poor throughput under heavy concurrent load, or vice versa.

How can multi-step agent workflows manage compounding latency?

Running independent steps in parallel rather than strictly sequentially where possible, routing simple sub-tasks to smaller faster models, and streaming partial responses to the user rather than waiting for the entire multi-step process to complete before showing anything.

Does a bigger, more capable model always mean higher latency?

Generally yes, larger models typically take longer per response than smaller ones, which is a core reason for routing simple, latency-sensitive sub-tasks to a small language model and reserving the larger model for steps where its extra capability actually matters.