Error Event - Interactly API

The error event is triggered when critical errors occur during call processing, specifically when Large Language Model (LLM) or Speech-to-Text (STT) services fail.

Error events indicate technical failures that may cause conversation interruptions or call termination. These require immediate attention for maintaining service quality.

When It’s Triggered

This event is sent when:

LLM Errors: Language model API failures, timeouts, or service unavailability
STT Errors: Speech-to-text conversion failures or transcription service issues
Authentication Issues: API key problems or authorization failures
Quota Exceeded: Service limits reached for LLM or STT providers
Network Errors: Connectivity issues affecting external service calls

Error Categories

LLM Errors

Provider API downtime or maintenance
Rate limiting exceeded
Model capacity limitations
Invalid API keys or expired tokens
Malformed requests or unsupported parameters
Content filtering or safety violations

STT Errors

Audio quality too poor for transcription
Unsupported audio format or codec
Service provider outages
Network connectivity issues
Authentication or quota problems
Language detection failures

Event Structure

{
    "message": {
        "timestamp": 1772702495000,
        "type": "error",
        "call": { /* Call Object */ },
        "assistant": { /* Assistant Object */ },
        "messages": [ /* Conversation until error */ ],
        "phone": { /* Phone Object */ },
        "customer": { /* Customer Object */ },
        "analysis": { /* Empty during error */ },
        "error": { /* Error Details */ }
    }
}

Key Fields

Field	Type	Description
`message.type`	string	Always “error” for this event
`message.timestamp`	number	Unix timestamp when error occurred
`error.type`	string	Error category: “llm_error” or “stt_error”
`error.code`	string	Specific error code from provider
`error.message`	string	Human-readable error description
`error.provider`	string	Service provider that failed
`error.retryable`	boolean	Whether the error can be retried

Call Object

The call object contains comprehensive information about the call session.

Field	Type	Description
`id`	string	Unique call identifier (e.g., “WC-82015760-c3bd-427d-a23b-ba9b07e4ab85”)
`teamId`	string	Organization/team identifier
`assistantId`	string	ID of the assistant handling this call
`callType`	string	Type of call: “web”, “phone”, etc.
`direction`	string	Call direction: “inbound” or “outbound”
`startAt`	string	ISO timestamp when call started
`endAt`	string	ISO timestamp when call ended (only in end-of-call-report)
`userNumber`	string	User’s phone number or identifier
`assistantNumber`	string	Assistant’s number or identifier
`status`	string	Current call status: “queued”, “ongoing”, “finished”, “forwarded”
`phoneCallStatus`	string	Detailed phone status: “in-progress”, “completed”, etc.
`phoneCallStatusReason`	string	Human-readable status reason
`callEndTriggerBy`	string	What triggered call end: “bot”, “user”, “system”
`assistantCallDuration`	number	Duration of call in milliseconds
`analysis`	analysis-object	Call analysis results
`recording`	object	Recording information with S3 bucket and path
`assistantOverrides`	object	Dynamic variables and validation overrides
`metadata`	object	Custom metadata associated with the call
`cost`	object	Cost breakdown (only in end-of-call-report)
`metrics`	object	Detailed call metrics (only in end-of-call-report)

Assistant Object

The assistant object contains the configuration and settings of the assistant handling the call.

In some webhook events, the assistant object may be truncated for brevity. The full assistant configuration is typically included in status-update events.

Field	Type	Description
`_id`	string	Unique assistant identifier
`name`	string	Display name of the assistant
`welcomeMessage`	string	Message played when call starts
`welcomeMessageMode`	string	How welcome message is triggered: “automatic”, “manual”
`welcomeMessageInterruptionsEnabled`	boolean	Whether users can interrupt welcome message
`endCallMessage`	string	Message played when call ends
`endCallPhrases`	array	Phrases that trigger call termination
`bargeInEnabled`	boolean	Whether users can interrupt assistant responses
`assistantProvider`	string	LLM provider: “openai”, “anthropic”, “gemini”, etc.
`assistantModel`	string	Specific model being used
`assistantSystemPrompt`	string	System prompt defining assistant behavior
`assistantTemperature`	number	LLM creativity setting (0.0 to 1.0)
`assistantMaxTokens`	number	Maximum tokens per response
`assistantAnalysis`	object	Configuration for call analysis features
`assistantServer`	object	Webhook configuration for this assistant
`config`	object	Speech-to-text and text-to-speech configurations

Key Subobjects

assistantAnalysis: Contains settings for summary generation, success evaluation, and structured data extraction
assistantServer: The webhook configuration that triggered this event
config.speech: STT/TTS vendor settings, voice configuration, and language options

Messages Object

The messages array contains the conversation history between the user and assistant.

Each conversation-update event includes all messages from the start of the call up to that point, not just the latest message. This ensures that if any individual event is missed, you still have the complete conversation context.

Message Structure

Field	Type	Description
`messageId`	string	Unique message identifier
`role`	string	Message sender: “user” or “assistant”
`text`	string	Transcribed or generated message content
`timestamp`	number	Unix timestamp in milliseconds
`metrics`	object	Performance metrics for this message
`skippedAssistantMessages`	array	Assistant messages that were skipped due to interruptions

Message Metrics

Each message includes detailed performance metrics:

Timeline Metrics

totalElapsedTimeMs: Total time since call start
offsetFromPreviousTurnMs: Time gap from previous message

Audio Metrics

totalAudioReceivedMs: Total audio duration received
audioDelayPerTurnMs: Audio processing delay for this turn
delayedPacketsPerTurnCount: Count of delayed network packets

Speech-to-Text (STT) Metrics

timestampMs: When transcription was completed
startOffsetMs/endOffsetMs: Audio segment boundaries
confidence: Transcription confidence score (0.0 to 1.0)
vadMs: Voice activity detection duration

Text-to-Speech (TTS) Metrics

audioDurationMs: Duration of generated audio
generationTimeMs: Time to generate audio
queueLatencyMs: Processing queue delay
isCachePlaying: Whether audio was served from cache

LLM Metrics

responseLatencyMs: Time for LLM to generate response
queueLatencyMs: Processing queue delay

Message Types

Welcome Messages: Have messageId starting with “welcome-”
User Messages: Have messageId starting with “user-”
Assistant Messages: Have complex messageId with user reference and timestamp

Example Payloads

{
  "message": {
    "timestamp": 1772702495000,
    "type": "error",
    "call": {
      "id": "WC-82015760-c3bd-427d-a23b-ba9b07e4ab85",
      "status": "ongoing",
      "assistantId": "697769ef5e6d94d5ad83e01e"
    },
    "assistant": {
      "_id": "697769ef5e6d94d5ad83e01e",
      "name": "Mary Dental - main",
      "assistantProvider": "openai",
      "assistantModel": "gpt-4"
    },
    "error": {
      "type": "llm_error",
      "code": "rate_limit_exceeded",
      "message": "Rate limit exceeded for organization",
      "provider": "openai",
      "retryable": true,
      "details": {
        "limit_type": "requests_per_minute",
        "current_usage": 150,
        "limit": 100
      }
    },
    "messages": [
      {
        "messageId": "user-1",
        "role": "user",
        "text": "I need to schedule an appointment",
        "timestamp": 1772702490000
      }
    ]
  }
}

Error Handling Strategies

Automatic Retry Logic

def handle_error_event(event_data):
    error = event_data["message"]["error"]
    call = event_data["message"]["call"]

    if error["retryable"]:
        # Implement exponential backoff
        retry_count = get_retry_count(call["id"], error["type"])

        if retry_count < MAX_RETRIES:
            delay = min(2 ** retry_count, MAX_DELAY)
            schedule_retry(call["id"], delay, error["type"])

            logger.warning(f"Retrying {error['type']} for call {call['id']} "
                          f"in {delay}s (attempt {retry_count + 1})")
        else:
            # Max retries exceeded - escalate
            escalate_error(call["id"], error)
    else:
        # Non-retryable error - immediate escalation
        escalate_error(call["id"], error)

Provider Failover

def implement_provider_failover(event_data):
    error = event_data["message"]["error"]
    call = event_data["message"]["call"]
    assistant = event_data["message"]["assistant"]

    if error["type"] == "llm_error":
        # Switch to backup LLM provider
        current_provider = assistant["assistantProvider"]

        fallback_providers = {
            "openai": "anthropic",
            "anthropic": "google",
            "google": "openai"
        }

        backup_provider = fallback_providers.get(current_provider)
        if backup_provider:
            switch_llm_provider(call["id"], backup_provider)
            logger.info(f"Switched call {call['id']} from {current_provider} to {backup_provider}")

    elif error["type"] == "stt_error":
        # Switch to backup STT provider
        switch_stt_provider(call["id"], "deepgram_nova")
        logger.info(f"Switched STT provider for call {call['id']}")

Graceful Degradation

def handle_graceful_degradation(event_data):
    error = event_data["message"]["error"]
    call = event_data["message"]["call"]

    if error["type"] == "llm_error":
        # Use pre-defined fallback responses
        fallback_responses = [
            "I apologize for the technical difficulty. Let me connect you with a human agent.",
            "I'm experiencing a temporary issue. Please hold while I transfer you.",
            "Due to a technical problem, I'll need to forward your call to our support team."
        ]

        response = random.choice(fallback_responses)
        send_fallback_message(call["id"], response)
        initiate_human_handoff(call["id"])

    elif error["type"] == "stt_error":
        # Ask user to speak more clearly
        send_clarification_message(call["id"],
            "I'm having trouble hearing you clearly. Could you please repeat that?")

Real-time Alerting

def alert_on_error(event_data):
    error = event_data["message"]["error"]
    call = event_data["message"]["call"]

    alert_severity = determine_severity(error)

    alert_data = {
        "alert_type": "service_error",
        "severity": alert_severity,
        "error_type": error["type"],
        "error_code": error["code"],
        "provider": error["provider"],
        "call_id": call["id"],
        "assistant_id": call["assistantId"],
        "retryable": error["retryable"],
        "timestamp": event_data["message"]["timestamp"]
    }

    if alert_severity == "critical":
        send_immediate_alert(alert_data)
        page_on_call_engineer(alert_data)
    else:
        log_error_for_analysis(alert_data)

def determine_severity(error):
    # Critical: Authentication failures, quota exceeded
    # High: Provider outages, repeated failures
    # Medium: Temporary network issues
    # Low: Individual request failures

    critical_codes = ["authentication_failed", "quota_exceeded", "service_unavailable"]
    if error["code"] in critical_codes:
        return "critical"
    elif not error["retryable"]:
        return "high"
    else:
        return "medium"

Error Analysis and Monitoring

Error Rate Tracking

def track_error_metrics(event_data):
    error = event_data["message"]["error"]
    call = event_data["message"]["call"]

    # Track error rates by provider
    metrics.increment("errors.total", tags={
        "error_type": error["type"],
        "provider": error["provider"],
        "error_code": error["code"],
        "assistant_id": call["assistantId"]
    })

    # Track error distribution over time
    hour = datetime.fromtimestamp(
        event_data["message"]["timestamp"] / 1000
    ).hour

    metrics.increment("errors.by_hour", tags={
        "hour": hour,
        "error_type": error["type"]
    })

Root Cause Analysis

def analyze_error_patterns():
    # Get recent errors
    recent_errors = db.error_events.find({
        "timestamp": {"$gte": datetime.utcnow() - timedelta(hours=24)}
    })

    analysis = {
        "error_spike": detect_error_spikes(recent_errors),
        "provider_health": assess_provider_health(recent_errors),
        "assistant_issues": find_problematic_assistants(recent_errors),
        "temporal_patterns": analyze_time_patterns(recent_errors)
    }

    # Generate actionable insights
    for insight in generate_insights(analysis):
        if insight["confidence"] > 0.8:
            create_incident_ticket(insight)

def detect_error_spikes(errors):
    # Group errors by 10-minute windows
    windows = defaultdict(int)
    for error in errors:
        window = error["timestamp"] // (10 * 60 * 1000) * (10 * 60 * 1000)
        windows[window] += 1

    # Detect unusual spikes
    values = list(windows.values())
    if values:
        mean = statistics.mean(values)
        stdev = statistics.stdev(values) if len(values) > 1 else 0
        threshold = mean + (2 * stdev)

        spikes = [(k, v) for k, v in windows.items() if v > threshold]
        return spikes
    return []

Error Prevention

Proactive Monitoring

# Set up early warning systems
MONITORING_THRESHOLDS = {
    "llm_error_rate": {
        "warning": 0.02,   # 2% error rate
        "critical": 0.05,  # 5% error rate
        "window": "5m"
    },
    "stt_error_rate": {
        "warning": 0.01,   # 1% error rate
        "critical": 0.03,  # 3% error rate
        "window": "5m"
    },
    "provider_response_time": {
        "warning": 3000,   # 3 seconds
        "critical": 5000,  # 5 seconds
        "window": "1m"
    }
}

Configuration Optimization

{
    "assistantProvider": "openai",
    "assistantModel": "gpt-4o-mini",  // More reliable than newer models
    "assistantMaxTokens": 150,        // Shorter responses = less prone to errors
    "assistantTemperature": 0.3,      // Lower temperature = more consistent
    "config": {
        "speech": {
            "stt": {
                "vendor": "deepgram",
                "model": "nova-2",      // Stable, well-tested model
                "profanityFilter": true,
                "hints": ["appointment", "doctor", "clinic"]  // Domain-specific hints
            }
        }
    }
}

Error Recovery Testing

def test_error_scenarios():
    # Simulate various error conditions
    test_scenarios = [
        {"type": "rate_limit", "provider": "openai"},
        {"type": "auth_failure", "provider": "anthropic"},
        {"type": "audio_quality", "provider": "deepgram"},
        {"type": "network_timeout", "provider": "elevenlabs"}
    ]

    for scenario in test_scenarios:
        result = simulate_error_scenario(scenario)
        assert_error_handling_works(result)

User Communication

When errors occur, transparent communication improves user experience:

ERROR_USER_MESSAGES = {
    "llm_error": [
        "I'm experiencing a brief technical issue. Please give me a moment.",
        "Let me think about that - I'm processing your request."
    ],
    "stt_error": [
        "I didn't catch that clearly. Could you please repeat?",
        "I'm having trouble hearing you. Could you speak a bit louder?"
    ],
    "quota_exceeded": [
        "I'm currently at capacity. Let me transfer you to a human agent.",
        "Due to high demand, I'll connect you with our support team."
    ]
}

Related Events

Error events may be preceded by hang events if processing delays occur before the error

Error Event Examples

This document contains examples of different types of error webhook events.

LLM Rate Limit Error

{
    "message": {
        "timestamp": 1772702495000,
        "type": "error",
        "call": {
            "id": "WC-82015760-c3bd-427d-a23b-ba9b07e4ab85",
            "teamId": "67c0231ae6880fe48ef929ee",
            "assistantId": "697769ef5e6d94d5ad83e01e",
            "callType": "web",
            "direction": "inbound",
            "startAt": "2026-03-05T09:21:20.063Z",
            "userNumber": "web-Ramesh Naik",
            "assistantNumber": "697769ef5e6d94d5ad83e01e",
            "status": "ongoing",
            "phoneCallStatus": "in-progress",
            "phoneCallStatusReason": "Call is in progress",
            "callEndTriggerBy": "",
            "assistantCallDuration": 15000,
            "analysis": {
                "summary": "",
                "successEvaluation": ""
            },
            "recording": {
                "s3Bucket": "",
                "path": ""
            },
            "assistantOverrides": {},
            "metadata": {}
        },
        "assistant": {
            "_id": "697769ef5e6d94d5ad83e01e",
            "name": "Mary Dental - main",
            "assistantProvider": "openai",
            "assistantModel": "gpt-4",
            "assistantSystemPrompt": "You here are a voice assistant for Apollo clinic..."
        },
        "messages": [
            {
                "messageId": "welcome-1",
                "role": "assistant",
                "text": "Welcome to Apollo clinic!!",
                "timestamp": 1772702480279
            },
            {
                "messageId": "user-1",
                "role": "user",
                "text": "I need to schedule an appointment please",
                "timestamp": 1772702490000
            }
        ],
        "phone": {
            "provider": {
                "name": ""
            }
        },
        "customer": {
            "number": "web-Ramesh Naik"
        },
        "analysis": {},
        "error": {
            "type": "llm_error",
            "code": "rate_limit_exceeded",
            "message": "Rate limit exceeded for organization",
            "provider": "openai",
            "retryable": true,
            "details": {
                "limit_type": "requests_per_minute",
                "current_usage": 150,
                "limit": 100,
                "reset_time": "2026-03-05T09:23:00Z"
            }
        }
    }
}

STT Audio Quality Error

{
    "message": {
        "timestamp": 1772702485000,
        "type": "error",
        "call": {
            "id": "WC-82015760-c3bd-427d-a23b-ba9b07e4ab85",
            "teamId": "67c0231ae6880fe48ef929ee",
            "assistantId": "697769ef5e6d94d5ad83e01e",
            "status": "ongoing"
        },
        "assistant": {
            "_id": "697769ef5e6d94d5ad83e01e",
            "name": "Mary Dental - main"
        },
        "messages": [
            {
                "messageId": "welcome-1",
                "role": "assistant",
                "text": "Welcome to Apollo clinic!!",
                "timestamp": 1772702480279
            }
        ],
        "phone": {
            "provider": {
                "name": ""
            }
        },
        "customer": {
            "number": "web-Ramesh Naik"
        },
        "analysis": {},
        "error": {
            "type": "stt_error",
            "code": "audio_quality_insufficient",
            "message": "Audio quality too low for accurate transcription",
            "provider": "deepgram",
            "retryable": false,
            "details": {
                "confidence_score": 0.12,
                "noise_level": "high",
                "signal_to_noise_ratio": -5,
                "suggested_action": "request_user_repeat"
            }
        }
    }
}

General

Quickstart

Calls

Campaigns

Assistant Tools

Webhooks

Vendors

​When It’s Triggered

​Error Categories

​LLM Errors

​STT Errors

​Event Structure

​Key Fields

​Call Object

​Assistant Object

​Key Subobjects

​Messages Object

​Message Structure

​Message Metrics

​Timeline Metrics

​Audio Metrics

​Speech-to-Text (STT) Metrics

​Text-to-Speech (TTS) Metrics

​LLM Metrics

​Message Types

​Example Payloads

​Error Handling Strategies

​Automatic Retry Logic

​Provider Failover

​Graceful Degradation

​Real-time Alerting

​Error Analysis and Monitoring

​Error Rate Tracking

​Root Cause Analysis

​Error Prevention

​Proactive Monitoring

​Configuration Optimization

​Error Recovery Testing

​User Communication

Related Events

​Error Event Examples

​LLM Rate Limit Error

​STT Audio Quality Error

When It’s Triggered

Error Categories

LLM Errors

STT Errors

Event Structure

Key Fields

Call Object

Assistant Object

Key Subobjects

Messages Object

Message Structure

Message Metrics

Timeline Metrics

Audio Metrics

Speech-to-Text (STT) Metrics

Text-to-Speech (TTS) Metrics

LLM Metrics

Message Types

Example Payloads

Error Handling Strategies

Automatic Retry Logic

Provider Failover

Graceful Degradation

Real-time Alerting

Error Analysis and Monitoring

Error Rate Tracking

Root Cause Analysis

Error Prevention

Proactive Monitoring

Configuration Optimization

Error Recovery Testing

User Communication

Error Event Examples

LLM Rate Limit Error

STT Audio Quality Error