Hi team, We are facing interruption handling issues with LiveKit Agents and wanted to understand the recommended architecture here. Setup: • Self-hosted LiveKit server on AWS Mumbai (

ap-south-1

) • Using LiveKit Cloud credentials (Build Plan) • Initially using

vad

interruption handling Issue: With normal

vad

mode, the agent occasionally got stuck during conversations even after configuring:

resume_false_interruption = 0.8

So we switched to

adaptive

interruption handling. However, after testing adaptive mode, we observed: 1. Genuine interruptions are sometimes missed, especially in noisy environments 2. Frequent timeout/fallback errors:

adaptive interruption inference timed out after 0.7s
fallback to VAD
status_code=408

From logs, it seems adaptive inference is happening on:

agent-gateway.livekit.cloud

So the flow becomes: Mumbai Server → LiveKit Cloud (US/EU) → Mumbai This cross-region round trip appears to exceed the strict

0.7s

timeout budget under slight latency/noise conditions. 3. Additionally, it seenms When adaptive inference times out and fallback to VAD happens, the fallback VAD does not appear to properly respect/load

min_interruption_duration

. As a result: • interruption detection becomes unstable • short/noisy audio chunks trigger inconsistent states • the agent sometimes gets stuck in an unusable state ("state 0" behavior on our side) This makes the fallback path unreliable in production. Questions: 1. Is this a known limitation/bug with adaptive → VAD fallback handling. Is there any workaround/recommended configuration for stable fallback behavior? 2. Is there any way to self-host the adaptive interruption inference/VAD model on our own infrastructure? 3. Since we already have LiveKit Cloud Build Plan credentials, is there a recommended low-latency setup for hybrid deployments (self-hosted LiveKit + cloud adaptive inference)? Currently, local

vad

mode feels more stable than adaptive mode due to these timeout/fallback issues. Would appreciate guidance on the recommended production architecture and whether this fallback behavior is a known issue.

Thanks for reaching out, Harleen. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

suggest me solutions

Thanks — I’ll give concrete, prioritized solutions you can try right away plus what I’ll need if you want tuned values. Priority path (fast, highest chance to stabilize) • Switch to VAD-only interruption handling: set

interruption.mode = "vad"

. This removes Cloud round-trips and avoids adaptive timeouts. • Tune VAD thresholds: ◦ Increase

min_interruption_duration

(e.g., from 0.5s → 0.8–1.0s) to ignore short noisy spikes. ◦ Increase

resume_false_interruption

(you already set 0.8; try 0.9 if false resumes persist). ◦ Optionally set

min_interruption_words

>0 if you have speech-to-text aligned transcripts. • Add client-side audio pre-processing: noise suppression, AGC, and voice activity smoothing to reduce spurious triggers. If you prefer adaptive behavior (higher accuracy) but avoid timeouts • Move the agent runtime/media to LiveKit Cloud so inference and media are co-located (no cross-region RTT). • If migration isn’t possible, reduce network latency to Cloud (place self-hosted servers in a region with low RTT to the Cloud inference endpoint). Note: this is fragile vs full Cloud. Mitigations for fallback instability • Log timestamps for adaptive request start/finish and VAD decisions to confirm VAD parameters are applied during fallback. • Run controlled tests with synthetic noisy audio to reproduce the failure; capture Agent Console visualizations for each case. • If fallback still seems to ignore

min_interruption_duration

, collect logs and samples for further diagnosis. Operational improvements • Increase observability: log adaptive timeouts, VAD triggers, and agent state transitions. • Create replayable test cases (noisy vs clean) so you can validate config changes quickly. • Use progressive rollout: test tuned VAD in a small % of calls before full roll-out. Which option should we prioritize? And if you want tuning recommendations, please share: • Agent SDK and version (Python/TS), • Current interruption config (full block), • Measured RTT from your Mumbai server to agent-gateway.livekit.cloud (ms), • Example logs showing an adaptive timeout + fallback sequence. I’ll return exact config values and test steps once you share those details. , with interactive elements

f we decide to stay with local

vad

for production stability, what would be the recommended tuning strategy to reduce: • false barge-ins • interruption misses • agent getting stuck after noisy interruptions Our current configuration is:

LIVEKIT_MIN_INTERRUPTION_DURATION=0.5
LIVEKIT_RESUME_FALSE_INTERRUPTION=true
LIVEKIT_FALSE_INTERRUPTION_TIMEOUT=0.8

Observed behavior: • Lower interruption duration improves responsiveness but increases false interruptions in noisy environments • Increasing it reduces false barge-ins but sometimes misses genuine user interruptions • Occasionally the agent still enters a stuck/interrupted state after noise-triggered interruptions Are there recommended production-safe ranges or additional parameters we should tune for: • noisy environments • telephony-like audio • real-time conversational agents

Thanks — I’ll respond to your tuning request for staying with local VAD. I’ll highlight recommended parameter ranges, practical tuning steps, and monitoring to reduce false barge-ins, missed interruptions, and the “stuck” state in noisy or telephony-like environments. Quick summary of what I’m addressing • You’ll tune

min_interruption_duration

, false-interruption controls, and related settings to balance responsiveness vs stability for noisy and telephony audio, and add operational steps to avoid stuck states. Recommended parameter ranges (start here and iterate) •

min_interruption_duration

(LIVEKIT_MIN_INTERRUPTION_DURATION) ◦ Noisy environments: 0.8 – 1.2 s ◦ Telephony / clean voice: 0.4 – 0.7 s ◦ Real-time conversational agents (balanced): 0.6 – 0.9 s •

resume_false_interruption

(LIVEKIT_RESUME_FALSE_INTERRUPTION) ◦ Keep true to enable resume protection; use conservative thresholds in config that represent required confidence (you already enabled it). •

false_interruption_timeout

(LIVEKIT_FALSE_INTERRUPTION_TIMEOUT) ◦ Noisy: 0.9 – 1.5 s ◦ Telephony: 0.6 – 1.0 s •

min_interruption_words

(if STT with aligned transcripts available) ◦ Set to 1–2 for telephony/voice to require at least a word before confirming interruption (reduces short false triggers). • Aggressive smoothing / debounce (if available) ◦ Apply short smoothing window of 100–250 ms for VAD decisions before flipping interruption state. Tuning strategy (step-by-step) 1. Baseline collection ◦ Record representative samples: quiet, typical, noisy, telephony carrier noise, overlapping speech. ◦ Collect current call logs with VAD events and timestamps. 1. Conservative safety-first pass ◦ Set

min_interruption_duration

to the upper bound for your class (start 0.9s for noisy). ◦ Set

false_interruption_timeout

to 1.2s. ◦ Enable

resume_false_interruption

. ◦ If STT synced, set

min_interruption_words = 1

. ◦ Deploy to a small % of traffic and monitor. 1. Responsiveness tuning (only after stability) ◦ Lower

min_interruption_duration

in 0.1s steps until you hit acceptable responsiveness without rising false positives. ◦ Reduce

false_interruption_timeout

similarly if resumes feel too slow. 1. Noise-specific tweaks ◦ For telephony: prefer slightly lower

min_interruption_duration

(0.4–0.7s) plus

min_interruption_words

. ◦ For heavy ambient noise: prefer larger durations + stronger smoothing/debounce. Preventing the agent from getting “stuck” • Ensure

resume_false_interruption

is enabled (you have this true). • Add an upper bound watchdog: if agent remains “interrupted” for > N seconds (e.g., 8–12s) without valid STT or user audio, force a resume/reset of turn state and log the event. • On fallback events, log VAD decision inputs (energy, duration) so you can verify parameters were respected. • Use a short backoff/retry: if multiple short noise-triggered interruptions occur, increase debounce temporarily. Audio pre-processing (high impact) • Add noise suppression and AGC before VAD. • Apply bandpass filtering for telephony (300–3400 Hz) when appropriate. • Normalize levels and remove DC offset. Observability & testing • Instrument: VAD on/off timestamps, interruption durations, resume events, fallback occurrences, and STT-aligned transcripts. • Create replayable test cases (scripts with recorded noisy and clean calls) to validate any param change automatically. • Use Agent Console visualizations to inspect decision timelines for problematic calls. Example starting configs • Noisy environments:

LIVEKIT_MIN_INTERRUPTION_DURATION=0.9
  LIVEKIT_RESUME_FALSE_INTERRUPTION=true
  LIVEKIT_FALSE_INTERRUPTION_TIMEOUT=1.2
  LIVEKIT_MIN_INTERRUPTION_WORDS=0   # or 1 if STT aligned

• Telephony / low-bandwidth:

LIVEKIT_MIN_INTERRUPTION_DURATION=0.5
  LIVEKIT_RESUME_FALSE_INTERRUPTION=true
  LIVEKIT_FALSE_INTERRUPTION_TIMEOUT=0.8
  LIVEKIT_MIN_INTERRUPTION_WORDS=1

When to consider switching approach • If even after tuning you still see frequent misses/timeouts or stuck states in production, consider: ◦ Moving agents/media to LiveKit Cloud for adaptive inference, or ◦ Retaining VAD locally but adding server-side business logic (watchdog resets, additional STT checks) to guard state transitions. If you want, I can propose exact values tailored to your environment — please share: • Representative RTT and packet loss from Mumbai servers to your clients, • A short set of sample audio logs (noisy vs clean) or the Agent SDK/version you’re running, • Whether you have STT with aligned transcripts enabled. Which environment should I tune for first: noisy/telephony/real-time balanced? , with interactive elements