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Thanks — I’m responding to your question about reducing interruptions and large latency when an agent initializes at the start of an outbound call (including cases where pre-generated TTS still gets interrupted). Below are practical techniques you can apply and the trade-offs for each. Key actions you can take • Prewarm the agent process and load heavy models (VAD, ASR, LLM runtime hooks) at worker startup so no expensive model init happens on call pickup. This eliminates cold-start delay. • Use preemptive generation so LLM + TTS begin producing output as soon as partial STT arrives (speculative generation). This shortens perceived response time when endpointing or VAD is slow. • Have the agent speak immediately after call connect via a controlled initial reply (call it a short greeting or “please hold”) using

generate_reply()

in the on-enter logic. This ensures the agent owns the floor while finishing setup. • Protect the initial utterance: set

allow_interruptions=false

(or equivalent) for the first critical prompts so brief ingress or noise doesn’t cancel initialization speech. • Enable false-interruption recovery: configure

resume_false_interruption=true

with a short

false_interruption_timeout

so accidental interruptions cause the agent to resume rather than abandon the TTS. • Improve endpointing/turn detection: tune endpointing delays (min/max) or use a faster turn-detector model so you detect end-of-speech quicker and avoid waiting too long before starting generation. • Dispatch the agent explicitly before the call connects for outbound flows (explicit agent dispatch). This avoids race conditions between call connect and agent startup. • Optimize network placement and observe TTFT/TTFB: colocate agent server near your STT/LLM/TTS providers and monitor Time To First Token/Byte to find bottlenecks. When to use which technique • Immediate gap at pickup (silence while agent boots): prewarm + immediate guarded greeting with

allow_interruptions=false

. • Perceived slow response despite pre-generated TTS: add preemptive generation so LLM/TTS start before final endpoint, and enable false-interruption resume. • Frequent accidental interruptions/noise: tighten VAD/endpointing, increase initial utterance protection, and enable resume on false interruption. • Race between agent assignment and call connect: use explicit dispatch so agent is already bound to the room when the callee answers. Short checklist to implement quickly 1. Prewarm VAD/models on worker start. 2. Dispatch agent before dialing. 3. On connect, call

generate_reply()

for a short guarded greeting with

allow_interruptions=false

. 4. Enable

preemptive_generation

for the session. 5. Set

resume_false_interruption=true

and a small

false_interruption_timeout

(e.g., ~1–2s). 6. Tune endpointing parameters to reduce detection lag. 7. Monitor TTFT/TTFB and move servers closer to AI services if needed. If you’d like, tell me which SDK/agent setup you’re using (Python/Node, telephony vs. WebRTC flow) and I’ll give the exact code snippet and recommended parameter values for your environment. Sources: Simple Call Answering Agent | LiveKit docs | Pipeline Translator Agent | LiveKit docs | Agent session | LiveKit docs | Agent speech and audio | LiveKit docs | Collect recording consent with tasks | LiveKit docs