The agent is interrupting the user while he starts to respond to the agent's question. Here is a transcript

2:08
AI Interviewer
Can you walk me through your main responsibilities as Principal Software Engineer at Acme?
2:15
Vicky Grigoletti
Sure.
2:20
AI Interviewer
Please describe
2:23
Vicky Grigoletti
My main responsibilities as a principal software engineer were Um, Yeah.
2:28
AI Interviewer
Could you please
2:28
Vicky Grigoletti
Designing large-scale softwares?
2:29
Vicky Grigoletti
Um
2:50
Vicky Grigoletti
Yeah, it also involved architect. during software projects, creating high-level design for them, reviewing various designs across the organization. Um, Helping with Um,
2:54
AI Interviewer
Could you
2:54
Vicky Grigoletti
code quality and maintaining the software quality.

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

AI interviewer questions can be cutoff, but agent should not interrupt when candidate is speaking

Thanks — I understand you want the AI interviewer to be able to have its questions cut off but never interrupt the candidate while the candidate is speaking. That’s a good, clear requirement. What to do (simple, reliable approach) • Let the agent’s speech be interruptible, but disable interruptions when the system detects the candidate is speaking. Implement this by: a. Keep

allow_interruptions=True

for agent prompts so interviewer questions can be cut off when needed. b. When the system detects candidate audio (VAD or a “user is speaking” flag), temporarily set

allow_interruptions=False

for the agent’s next scheduled speech or block the agent from starting a new speak action until the candidate is finished. c. Tune endpointing/VAD so the candidate’s speech is detected reliably (increase endpointing delay or lower VAD sensitivity to avoid false negatives). Practical options you can use • Option A — SDK-level switch (preferred): ◦ Before calling

say()

/

generate_reply()

for interviewer speech, check a

candidate_is_speaking

flag. If true, set

allow_interruptions=False

or postpone the call until speech ends. • Option B — Event-driven control: ◦ Subscribe to voice activity events. On candidate start -> cancel or pause any pending agent speech. On candidate end -> resume agent speech with

allow_interruptions=True

. • Option C — Use

session.interrupt()

selectively: ◦ Allow interrupts only when you explicitly want them (e.g., short prompts), and otherwise avoid automatic interruptions for candidate turns. Tuning recommendations • Increase endpointing delay slightly so silence must last longer before the system stops detecting speech. • Lower VAD sensitivity to reduce false negatives that cause the agent to start early. • Test with varied microphone/noise conditions and adjust until candidate turns are rarely cut off. If you want, I can provide exact code snippets for your environment. Which SDK/language are you using (JavaScript or Python) and are you using VAD events or a server-side speech-detection flag?

im using python

Thanks — I’ll give a concise Python approach you can drop into your interviewer flow to ensure the agent’s questions can be cut off but never interrupt the candidate while they’re speaking. Key idea • Keep interviewer speech normally interruptible, but before starting any agent speak action check a

candidate_is_speaking

flag (driven by VAD events). If the candidate is speaking, postpone or set

allow_interruptions=False

until they stop. Example (conceptual Python using LiveKit-style methods)

# Pseudocode / conceptual; adapt to your SDK interfaces.
from threading import Event, Lock
import time

candidate_is_speaking = False
speaking_lock = Lock()
pending_agent_action = None

def on_vad_start():
    global candidate_is_speaking
    with speaking_lock:
        candidate_is_speaking = True
    # If agent is about to speak, cancel or mark that it must not interrupt
    cancel_pending_agent_or_disable_interrupt()

def on_vad_end():
    global candidate_is_speaking
    with speaking_lock:
        candidate_is_speaking = False
    # If there was an agent prompt deferred, run it now (allow interrupts)
    run_pending_agent_if_any()

def schedule_agent_speak(text, allow_interruptions=True):
    """
    Schedule an agent speech. If candidate is speaking, defer or force non-interruptible.
    """
    global pending_agent_action
    with speaking_lock:
        if candidate_is_speaking:
            # Defer: store the action to run when candidate finishes
            pending_agent_action = (text, allow_interruptions)
            return
    # Candidate not speaking now — start speaking with requested interrupt setting
    do_agent_speak(text, allow_interruptions=allow_interruptions)

def cancel_pending_agent_or_disable_interrupt():
    global pending_agent_action
    # Option 1: cancel pending agent speech entirely
    pending_agent_action = None
    # Option 2 (alternate): if agent is currently speaking, update that speech to be non-interruptible
    # depends on SDK support; e.g., update_speech(allow_interruptions=False)

def run_pending_agent_if_any():
    global pending_agent_action
    if not pending_agent_action:
        return
    text, allow_interruptions = pending_agent_action
    pending_agent_action = None
    # Run now with allow_interruptions=True so it can be cut off by the candidate if needed
    do_agent_speak(text, allow_interruptions=True)

def do_agent_speak(text, allow_interruptions=True):
    # Replace with your SDK call, e.g. client.say(...) or session.generate_reply(...)
    # Example signature:
    # session.say(text=text, allow_interruptions=allow_interruptions)
    print(f"Agent speaking: allow_interruptions={allow_interruptions}: {text}")

Practical notes and tuning • Use real VAD/voice events from your audio pipeline or LiveKit voice-activity events to set

candidate_is_speaking

. • If your SDK supports updating an in-progress speech to disable interruptions, prefer that to avoid abrupt cancellation. • Adjust endpointing: increase endpoint delay or lower VAD sensitivity so short pauses don’t falsely mark candidate as finished. • For short prompts where interviewer should be cut off quickly, call

schedule_agent_speak(..., allow_interruptions=True)

. For long monologues you can still allow interruptions but ensure you check

candidate_is_speaking

before starting. If you want, I can convert this into a concrete snippet using the exact Python SDK calls you use (what function do you call to make the agent speak —

say()

,

generate_reply()

, or another method?)

session: AgentSession[None] = AgentSession( llm=openai.LLM( base_url=metadata.service_endpoints.chat_completion_url, api_key=auth_token, metadata=metadata.ai_interview_data.model_dump(), ), stt=openai.STT(model="gpt-4o-transcribe"), tts=elevenlabs.TTS( voice_id=metadata.conversation_data.voice_id, language=metadata.conversation_data.language, model="eleven_multilingual_v2", # TODO : Add support for voice id / language from metadata ), turn_detection=MultilingualModel(), vad=ctx.proc.userdata["vad"], preemptive_generation=True, use_tts_aligned_transcript=True, ) this is my agent session

Thanks — I see you’re using an

AgentSession

with

turn_detection=MultilingualModel()

and

vad=ctx.proc.userdata["vad"]

. Below is a focused, practical Python pattern you can drop into your flow to ensure the interviewer’s questions remain interruptible normally, but never interrupt the candidate while they’re speaking. Key idea • Use your VAD events to maintain a

candidate_is_speaking

flag. Before starting any agent speech, check that flag. If the candidate is speaking, either defer the agent speak or start it with

allow_interruptions=False

only when you must start immediately. Prefer deferring until the candidate finishes. Code (adapt to your SDK method names; this uses your session object and typical patterns)

from threading import Lock

candidate_is_speaking = False
speaking_lock = Lock()
pending_agent_action = None

def on_vad_start():
    global candidate_is_speaking
    with speaking_lock:
        candidate_is_speaking = True
        # Cancel or mark pending agent action so agent won't start while candidate speaks
        global pending_agent_action
        pending_agent_action = None

def on_vad_end():
    global candidate_is_speaking
    with speaking_lock:
        candidate_is_speaking = False
        # If an agent action was deferred, run it now
        if pending_agent_action:
            text, allow_interruptions = pending_agent_action
            pending_agent_action = None
            # When resuming after candidate finished, allow interruptions so candidate can cut in
            session.say(text=text, allow_interruptions=True)

def schedule_agent_speak(text, allow_interruptions=True):
    """
    Call this instead of session.say(...) directly.
    Keeps agent speak interruptible normally, but defers if candidate currently speaking.
    """
    global pending_agent_action
    with speaking_lock:
        if candidate_is_speaking:
            # Defer until candidate stops speaking
            pending_agent_action = (text, allow_interruptions)
            return
    # Candidate not speaking: start speaking normally (interruptible)
    session.say(text=text, allow_interruptions=allow_interruptions)

Practical options if you need different behavior • Immediate but non-interruptible: If your flow must start agent speech immediately even while candidate speaks, call

session.say(..., allow_interruptions=False)

when

candidate_is_speaking

is True. This will prevent the agent from cutting into the candidate — but prefer deferral to avoid talking over the user. • Event-driven cancel: If an agent has already started speaking and candidate begins, cancel or convert that speech to non-interruptible if your SDK supports updating in-progress speech. Otherwise, handle by canceling the agent speech on

on_vad_start()

and optionally rescheduling. Tuning suggestions • Use the

turn_detection

model + VAD reliably: drive

candidate_is_speaking

from your LiveKit VAD events (the

vad

you passed in). • Increase endpoint delay / lower VAD sensitivity slightly so short pauses don’t trigger agent to start early. • Test with real callers and background noise to tune the timing. If you want, I can: • Convert the above to use

generate_reply()

instead of

say()

if that’s your call pattern. • Add sample wiring showing how to subscribe to your VAD events (where

on_vad_start

/

on_vad_end

get called). Which would you prefer?

session: AgentSession[None] = AgentSession(
        llm=openai.LLM(
            base_url=metadata.service_endpoints.chat_completion_url,
            api_key=auth_token,
            metadata=metadata.ai_interview_data.model_dump(),
        ),
        stt=openai.STT(model="gpt-4o-transcribe"),
        tts=elevenlabs.TTS(
            voice_id=metadata.conversation_data.voice_id,
            language=metadata.conversation_data.language,
            model="eleven_multilingual_v2",  # TODO : Add support for voice id / language from metadata
        ),
        turn_detection=MultilingualModel(),
        vad=ctx.proc.userdata["vad"],
        preemptive_generation=True,
        use_tts_aligned_transcript=True,
    )

    # sometimes background noise could interrupt the agent session, these are considered false positive interruptions
    # when it's detected, you may resume the agent's speech
    @session.on("agent_false_interruption")
    def _on_agent_false_interruption(ev: AgentFalseInterruptionEvent) -> None:
        <http://logger.info|logger.info>("false positive interruption, resuming")
        session.generate_reply(instructions=ev.extra_instructions or NOT_GIVEN)

    @session.on("user_input_transcribed")
    def _on_user_input_transcribed(event: UserInputTranscribedEvent) -> None:
        print(f"User input transcribed: {event}")

    @session.on("conversation_item_added")
    def _on_conversation_item_added(event: SDKConversationItemAddedEvent) -> None:
        # Log conversation item added event
        # Check if item has the necessary attributes before accessing
        if hasattr(event.item, "id") and hasattr(event.item, "role"):
            event_log = LiveKitEventLog(
                event_type="conversation_item_added",
                created_at=event.created_at,
                metadata=ConversationItemAddedEvent(
                    item_id=event.item.id,
                    role=event.item.role,
                ),
            )
            logged_events.append(event_log)
            <http://logger.info|logger.info>(f"Conversation item added event logged: {event_log}")

    # Silence monitoring - track when user last spoke
    user_last_spoke_time = time.time()

    @session.on("user_state_changed")
    def _on_user_state_changed(event: SDKUserStateChangedEvent) -> None:
        nonlocal user_last_spoke_time

        # Update last spoke time when user starts speaking
        if event.new_state == "speaking":
            user_last_spoke_time = time.time()
            logger.debug("User started speaking, resetting silence timer")

        # Log user state changed event
        event_log = LiveKitEventLog(
            event_type="user_state_changed",
            created_at=event.created_at,
            metadata=UserStateChangedEvent(
                old_state=event.old_state,
                new_state=event.new_state,
            ),
        )
        logged_events.append(event_log)
        <http://logger.info|logger.info>(f"User state changed event logged: {event_log}")

    @session.on("agent_state_changed")
    def _on_agent_state_changed(event: SDKAgentStateChangedEvent) -> None:
        nonlocal user_last_spoke_time

        user_last_spoke_time = time.time()

        # Log agent state changed event
        event_log = LiveKitEventLog(
            event_type="agent_state_changed",
            created_at=event.created_at,
            metadata=AgentStateChangedEvent(
                old_state=event.old_state,
                new_state=event.new_state,
            ),
        )
        logged_events.append(event_log)
        <http://logger.info|logger.info>(f"Agent state changed event logged: {event_log}")

    metrics_collector = MetricsCollector()
    metrics_collector.register_shutdown_callback(ctx)

    @session.on("metrics_collected")
    def _on_metrics_collected(ev: MetricsCollectedEvent) -> None:
        metrics_collector.handle_metrics_event(ev)

    # Silence monitoring task reference
    monitor_task = None

    async def monitor_interaction() -> None:
        """Monitor user silence and prompt if inactive for too long."""
        nonlocal user_last_spoke_time

        try:
            while True:
                if session.user_state == "away":
                    return
                time_since_last_spoke = time.time() - user_last_spoke_time
                if (
                    time_since_last_spoke > SILENCE_THRESHOLD
                    and session.agent_state != "speaking"
                ):
                    <http://logger.info|logger.info>(
                        f"User silent for {time_since_last_spoke:.1f}s, prompting user"
                    )
                    try:
                        # Make the agent interrupt and say "are you there?"
                        await session.say("Are you there?", allow_interruptions=True)
                        # Reset the timer after prompting to avoid repeated prompts
                        user_last_spoke_time = time.time()
                    except asyncio.CancelledError:
                        <http://logger.info|logger.info>("Silence monitor cancelled during prompt")
                        raise
                    except Exception as e:
                        logger.exception(f"Error while prompting user: {e}")
                else:
                    logger.debug(
                        f"User activity within threshold ({time_since_last_spoke:.1f}s)"
                    )

                # Check every second
                await asyncio.sleep(1)
        except asyncio.CancelledError:
            <http://logger.info|logger.info>("Silence monitoring task cancelled")
            raise

    @session.on("close")
    def on_close(ev: Any) -> None:
        print(f"Session closed. Reason: {ev.reason}")
        if ev.error:
            print(f"Error: {ev.error.type}, Message: {ev.error.error}")

    await session.start(
        agent=assistant,
        room=ctx.room,
        room_input_options=RoomInputOptions(
            noise_cancellation=noise_cancellation.BVC(),
            close_on_disconnect=False,
        ),
    )

    await ctx.connect()

    await ctx.wait_for_participant()

    # Send participant_connected events for any participants already in the room
    for _participant_sid, participant in ctx.room.remote_participants.items():
        print(f"Participant already connected: {participant.identity}")
        participant_connected_event = api.WebhookEvent(
            id=str(uuid.uuid4()),
            created_at=int(time.time()),
            event="participant_joined",
            participant=api.ParticipantInfo(
                identity=participant.identity,
                name=participant.name,
                metadata=participant.metadata,
                kind=participant.kind,
                attributes=participant.attributes,
                disconnect_reason=participant.disconnect_reason
            ),
            room=api.Room(
                name=ctx.room.name,
                metadata=ctx.room.metadata,
                num_participants=ctx.room.num_participants,
                num_publishers=ctx.room.num_publishers,
            ),
        )
        if metadata.service_endpoints.livekit_events_url:
            utils.send_webhook_event_background(
                metadata.service_endpoints.livekit_events_url,
                participant_connected_event,
            )


    # Have the agent say the initial message aloud (if mextadata was retrieved)
    initial_call_greeting = (
        metadata.conversation_data.initial_call_greeting
        if metadata.conversation_data.initial_call_greeting
        else "Hello! I'm your AI assistant. How can I help you today?"
    )
    await session.say(initial_call_greeting, allow_interruptions=False)
    
    # Add any participants already in the room to seen_identities
    for _, p in ctx.room.remote_participants.items():
        seen_identities.add(p.identity)

    # Start the silence monitoring task
    # monitor_task = asyncio.create_task(monitor_interaction())
    <http://logger.info|logger.info>("Silence monitoring task started")

    async def write_transcript() -> None:
        nonlocal logged_events, monitor_task

        # Cancel the silence monitoring task
        if monitor_task:
            <http://logger.info|logger.info>("Cancelling silence monitoring task")
            monitor_task.cancel()
            try:
                await monitor_task
            except asyncio.CancelledError:
                logger.warning("Silence monitoring task cancelled successfully")

        if egress_id:
            try:
                lkapi = api.LiveKitAPI()
                stop_request = api.StopEgressRequest(egress_id=egress_id)
                egress_info = await lkapi.egress.stop_egress(stop_request)
                <http://logger.info|logger.info>(f"Egress {egress_id} stopped successfully: {egress_info}")

                await lkapi.aclose()  # type: ignore[no-untyped-call]
            except Exception as egress_error:
                logger.error(f"Failed to stop egress {egress_id}: {egress_error}")

        filename = f"/tmp/transcript_{ctx.room.name}.json"
        transcript_data = session.history.items
        enhanced_transcript = RawTranscript.model_validate(
            {
                "items": transcript_data,
                "logged_events": [event.model_dump() for event in logged_events],
            }
        )
        print("enhanced_transcript: ", enhanced_transcript)

        # Save transcript to file
        with open(filename, "w") as f:
            json.dump(enhanced_transcript.model_dump(), f, indent=2)
        print(
            f"Transcript with {len(logged_events)} logged events for {ctx.room.name} saved to {filename}"
        )

        # Upload transcript to S3
        try:
            s3_path = f"ai_recruiter/livekit_transcripts/{group_id}/{call_session_id}_raw.json"
            upload_success = await upload_transcript_to_s3(
                transcript_data=enhanced_transcript.model_dump(),
                egress_credentials=egress_creds,
                s3_path=s3_path,
            )
            if upload_success:
                <http://logger.info|logger.info>(
                    f"Transcript uploaded to S3: s3://{egress_creds.bucket}/{s3_path}"
                )
            else:
                logger.error("Failed to upload transcript to S3")
        except Exception as e:
            logger.error(f"Error uploading transcript to S3: {e}")

        # Send transcript to webhook API
        try:
            await send_webhook_event(
                metadata.service_endpoints.post_call_hook_url,
                {"interview_session_id": interview_session_id, "group_id": group_id},
            )
            print(f"Transcript webhook with logged events sent for {ctx.room.name}")
        except Exception as e:
            logger.error(f"Failed to send transcript webhook: {e}")

    ctx.add_shutdown_callback(write_transcript)


if __name__ == "__main__":
    cli.run_app(
        WorkerOptions(
            entrypoint_fnc=entrypoint,
            prewarm_fnc=prewarm,
            agent_name="profile_screening_agent",
        )
    )

Can you update this

Thanks — I’ll update your session to prevent the agent from interrupting the candidate while they’re speaking, using your existing VAD /

user_state_changed

events and

allow_interruptions

behavior. Changes I’ll make: • Track a

candidate_is_speaking

flag driven by

user_state_changed

. • Wrap calls that cause the agent to speak with a helper

safe_say(...)

that defers or forces non-interruptible behavior when the candidate is speaking. • Resume deferred prompts after candidate stops. • Handle false-positive interruptions by resuming with

generate_reply(...)

(you already have this handler). Patch to drop into your code (replace or add where appropriate):

from threading import Lock
import asyncio

# Add near your other globals
candidate_is_speaking = False
_pending_agent_action = None
_speaking_lock = Lock()

def _defer_agent_action(text: str, allow_interruptions: bool):
    global _pending_agent_action
    _pending_agent_action = (text, allow_interruptions)

def _pop_pending_agent_action():
    global _pending_agent_action
    pa = _pending_agent_action
    _pending_agent_action = None
    return pa

async def safe_say(text: str, allow_interruptions: bool = True, defer_if_speaking: bool = True):
    """
    Use this instead of session.say(...) for interviewer prompts.
    - If candidate is speaking and defer_if_speaking=True -> defer until they stop.
    - If candidate is speaking and defer_if_speaking=False -> start with allow_interruptions=False
      to avoid cutting the candidate off (use sparingly).
    """
    with _speaking_lock:
        speaking = candidate_is_speaking

    if speaking and defer_if_speaking:
        # store for later; will resume on vad end
        _defer_agent_action(text, allow_interruptions)
        return

    if speaking and not defer_if_speaking:
        # Force non-interruptible so we don't cut off the candidate
        await session.say(text, allow_interruptions=False)
        return

    # Candidate not speaking: proceed normally
    await session.say(text, allow_interruptions=allow_interruptions)

# Wire into your user_state_changed handler (replace existing logic updating user_last_spoke_time)
@session.on("user_state_changed")
def _on_user_state_changed(event: SDKUserStateChangedEvent) -> None:
    nonlocal user_last_spoke_time, candidate_is_speaking

    # Update last spoke time when user starts speaking
    if event.new_state == "speaking":
        user_last_spoke_time = time.time()
        with _speaking_lock:
            candidate_is_speaking = True
        logger.debug("User started speaking, resetting silence timer")

        # If agent was speaking and you want to cancel agent speech on user start:
        # session.cancel_current_tts()  # if your SDK exposes a cancel; optional

    elif event.new_state in ("idle", "silent", "not_speaking"):
        # treat these as user finished speaking — adjust states as your system emits them
        with _speaking_lock:
            candidate_is_speaking = False
        logger.debug("User stopped speaking")

        # Run any deferred agent prompt now (allow interrupts so candidate can cut in)
        pa = _pop_pending_agent_action()
        if pa:
            text, allow_interruptions = pa
            # run the deferred speak in background (don't block the event loop)
            asyncio.create_task(session.say(text, allow_interruptions=True))

    # existing logging code...
    event_log = LiveKitEventLog(
        event_type="user_state_changed",
        created_at=event.created_at,
        metadata=UserStateChangedEvent(
            old_state=event.old_state,
            new_state=event.new_state,
        ),
    )
    logged_events.append(event_log)
    <http://logger.info|logger.info>(f"User state changed event logged: {event_log}")

# Replace direct session.say calls in your flow with safe_say where appropriate.
# Example: greeting should not interrupt candidate, so force non-interruptible (or defer)
await safe_say(initial_call_greeting, allow_interruptions=False, defer_if_speaking=False)

# In the silence monitor use safe_say so you don't interrupt mid-speech:
await safe_say("Are you there?", allow_interruptions=True, defer_if_speaking=True)

Notes and recommendations • Use

safe_say(..., defer_if_speaking=True)

for most prompts so the agent waits until the candidate stops. • Use

defer_if_speaking=False

only when you must start immediately but must not cut off the candidate — this will set

allow_interruptions=False

. • You already handle false positives with

agent_false_interruption

— keep that handler; it will call

generate_reply(...)

which resumes generation if an interruption was spurious. • If your SDK supports canceling in-progress TTS (e.g.,

session.cancel_current_tts()

), call it on user start to avoid overlapping audio. If you want, I can convert the defer resume to call

session.generate_reply(...)

instead of

session.say(...)

(useful if you rely on preemptive generation), or provide a precise cancel call for your TTS if you tell me if such a cancel method exists in your SDK.

This implementation looks too complex - Is there a way to fix this

Thanks — I hear you: you want a much simpler fix than the event-heavy approach. Short answer (one-line change) • Set allow_interruptions=False at the session level in your AgentSession creation. Where to change Replace or add this in your existing session init:

session: AgentSession[None] = AgentSession(
    ...
    vad=ctx.proc.userdata["vad"],
    preemptive_generation=True,
    use_tts_aligned_transcript=True,
    allow_interruptions=False,  # ← add this
)

What this does • Prevents the agent from cutting in while the candidate is speaking (no per-turn state tracking needed). • Keeps your rest of flow intact (handlers, silence monitor, false-interruption resume). If you prefer the agent to remain interruptible by the user but avoid premature cut-ins from short pauses instead, use tuning instead of turning interruptions off: • Increase endpointing delay: add

min_endpointing_delay=2.0

(seconds) to session. • Or keep

allow_interruptions=True

and set

min_interruption_duration

/

min_interruption_words

to make interruptions require longer speech. Which option do you want me to show in code — the simple global

allow_interruptions=False

or the tuning parameters? Sources: Turns overview | LiveKit docs | Uninterruptable Agent | LiveKit docs | Interrupt User | LiveKit docs | Collect recording consent with tasks | LiveKit docs | How does end-of-utterance detection work in conversations?