What is Latency?
AI latency has multiple components that must be optimised individually. Prefill latency is the time to process the input tokens (the prompt, retrieved documents, conversation history). Decode latency is the time to generate each output token, one at a time. Time-to-first-token (TTFT) is the most user-perceptible latency metric — how long before any output appears. For voice AI and real-time chat, streaming (showing output as it is generated rather than waiting for the complete response) can significantly improve perceived responsiveness even when total generation time is unchanged.
The latency budget for an agentic AI workflow is the sum of all its components: prompt processing time, any tool calls (each involving a network round-trip to an enterprise API), and generation time. A workflow that requires five tool calls, each taking 200ms to complete, adds one second of tool call latency alone. Production agentic AI engineering optimises the tool call graph — executing independent tool calls in parallel where possible, caching results of repeated calls, and pre-fetching data that is predictably needed in a workflow.
Also known as: Response Time, Inference Latency
Key Points
Core idea
For voice interactions to feel natural, the AI must respond within ~800ms of the user finishing speaking. Every millisecond of unnecessary latency degrades conversational experience.
Why it matters
Users perceive waiting for a response much more than the time it takes to complete. Streaming output (showing results as they generate) dramatically improves perceived responsiveness.
Enterprise use
Every API call in an agentic workflow adds latency. Parallel tool calls, smart caching, and pre-fetching are the primary techniques for managing agentic workflow latency in production.
How Latency works
Define the purpose, inputs, and success criteria that Latency must support.
Apply Latency in the relevant workflow while recording its inputs, configuration, and outputs.
Evaluate the result against representative data, operational constraints, and human review before expanding production use.
Sub-second voice AI response in production.
Fluid AI's voice AI platform is optimised for sub-second response latency in live customer interactions. On-premise deployment eliminates cloud round-trip overhead.
Explore Voice AITopics Covered
- AI latency enterprise optimization
- LLM latency production
- voice AI latency requirements
- time to first token enterprise AI
- on-premise AI latency advantage
- agentic AI latency tool calls
- AI response time enterprise
- real-time AI latency streaming