flowrpot
04/14/2026, 3:42 AMAI Evy
04/14/2026, 3:42 AMAI Evy
04/14/2026, 3:42 AMe2e - End-to-end latency from when the user stops talking to when the agent starts talking
- llm - LLM latency from the start of the LLM call to the first speakable chunk received
- tts - Text-to-speech latency
How to reduce latency:
If LLM inference is the bottleneck, switch to a faster model [(3) ]() . If TTS is the bottleneck, choose a lower-latency voice provider . If tool calls are slow, optimize tool endpoints or reduce response size .
You can also adjust agent responsiveness settings. The responsiveness parameter controls how responsive the agent is, with values ranging from 0 to 1 [(4) ]() . Higher values mean faster exchanges .AI Evy
04/14/2026, 3:42 AMAI Evy
04/14/2026, 3:42 AMAI Evy
04/14/2026, 3:43 AMflowrpot
04/14/2026, 3:43 AMAI Evy
04/14/2026, 3:44 AMAI Evy
04/14/2026, 3:44 AMe2e, llm, tts, and statistical metrics (p50, p90, min, max) .
2. **Adjust Responsiveness**: Set the responsiveness lower if you want the agent to wait longer before responding [(3) ]() . You can also enable "Dynamically adjust based on user input" to let the agent automatically tune its response timing during the call .
3. **Configure function speech behavior**: In your custom function settings, ensure "Speak after execution" is enabled so the agent continues talking after the function completes without waiting for user input [(1) ]() . This prevents awkward silences .
4. **Optimize your custom function endpoint**: The request will timeout in your specified timeout period, or 2 minutes if not specified . Make sure your endpoint responds quickly to avoid delays.
5. **Consider using backchanneling**: Configure how often and what words the agent uses to acknowledge users in the Speech Settings section [(3) ](AI Evy
04/14/2026, 3:44 AMAI Evy
04/14/2026, 3:44 AM