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Production Observability: Debug Voice AI in Minutes, Not Hours | SignalWire
Native Observability

Your Dashboards Say Green. Callers Disagree.

Multi-vendor voice AI gives you four dashboards with zero correlation. SignalWire gives you one structured event stream with per-component latency, barge-in analytics, and error classification.

1
trace per call, every component
10
error types, auto-classified
0
third-party APM tools required
2.7B
minutes and messages instrumented annually
The Observability Gap

Why Production Voice AI Breaks Invisibly

Component-Healthy, Experience-Broken

STT at 180ms, LLM at 900ms, TTS at 200ms, network at 150ms. Each vendor reports green. The caller hears a 1.4-second delay on every response. No single dashboard shows this.

Four Dashboards, Zero Correlation

Telephony logs in one dashboard, speech-to-text in another, language model in a third, text-to-speech in a fourth. When a call degrades, you spend 90 minutes correlating timestamps across systems.

No Audio-Level Insight

External vendors cannot capture barge-in at the audio level. You cannot see how much of the AI response the caller heard before interrupting, or why they interrupted.

Errors Without Classification

A failed call produces an error code in one vendor's logs and silence in the others. Was it a speech recognition failure, a model timeout, or a network partition? You have to reconstruct the answer manually.

Build a Voice AI Agent

from signalwire_agents import AgentBase
from signalwire_agents.core.function_result import SwaigFunctionResult

class SupportAgent(AgentBase):
    def __init__(self):
        super().__init__(name="Support Agent", route="/support")
        self.prompt_add_section("Instructions",
            body="You are a customer support agent. "
                 "Greet the caller and resolve their issue.")
        self.add_language("English", "en-US", "rime.spore:mistv2")

    @AgentBase.tool(name="check_order")
    def check_order(self, order_id: str):
        """Check the status of a customer order.

        Args:
            order_id: The order ID to look up
        """
        return SwaigFunctionResult(f"Order {order_id}: shipped, ETA April 2nd")

agent = SupportAgent()
agent.run()

Debugging a Slow Agent: 90 Minutes vs. 5 Minutes

Multi-Vendor Debugging

  • Check telephony dashboard: call connected normally (15 min)
  • Check STT dashboard: latency 180ms, normal (10 min)
  • Check LLM dashboard: rate limiting at peak, unclear which calls (20 min)
  • Check TTS dashboard: queue depth increasing (10 min)
  • Correlate timestamps across four dashboards (30 min)
  • Root cause found after 90 minutes of cross-vendor investigation

SignalWire Native Observability

  • Query call_timeline: STT 150ms, LLM 2,100ms (identified), TTS 160ms (2 min)
  • Check barge-in analytics: callers interrupt at 2.3s consistently (2 min)
  • Root cause identified, fix applied in under 5 minutes

What the Platform Instruments Automatically

SignalWhat It CapturesWhy It Matters
Per-component latencySTT, LLM, TTS, and tool call latency on every turnPinpoints which component is slow without cross-vendor correlation
Barge-in analyticsElapsed milliseconds, approximate text heard, turn contextReveals whether the AI talks too much, too slowly, or about the wrong thing
Step transitionsCause (model decision, tool result, timeout) with metadataShows why the conversation moved between steps
Error taxonomy10 types with fatal/non-fatal classification and recovery actionClassifies errors automatically so you debug by category, not by log line

Error Taxonomy: 10 Types, Automatic Recovery

Error TypeFatalPlatform Response
stt_failureNoRecovery phrase: asks caller to repeat
llm_timeoutNoRetry with fallback model
llm_errorNoRecovery phrase, log context
tts_failureNoFallback voice
tool_timeoutNoInform caller, retry
tool_errorNoError-specific recovery phrase
auth_failureYesRedirect flow
network_errorYesGraceful hangup with state capture
config_errorYesError message and log
system_errorYesGraceful hangup with hangup hook

From Incident to Resolution

1

Alert fires

Your monitoring detects elevated latency or increased error rate on voice AI calls.

2

Query call_timeline

Filter by time range, error type, or component. See per-component latency and error classification on every affected call.

3

Identify the root cause

The trace shows which component degraded, when it started, and how the platform attempted recovery.

4

Fix and verify

Update the prompt, swap the model, or adjust the timeout. Verify the fix in the same event stream.

💡
The call_timeline feed provides every event from every call in a flat, queryable format. Export to Snowflake, BigQuery, Redshift, or any warehouse for operational analytics at scale.

FAQ

Do I need to add instrumentation to my agent code?

No. Per-component latency, barge-in analytics, step transitions, and error classification are captured automatically by the platform. Your agent code requires zero instrumentation.

Can I use my existing APM tools?

Yes. The call_timeline feed exports structured events to any data warehouse or analytics pipeline. You can use your existing tools alongside native observability.

What are barge-in analytics?

Every time a caller interrupts the AI, the platform records how many milliseconds of audio played, what text the caller approximately heard, and what they said after interrupting. At scale, this reveals prompt optimization opportunities.

How does error recovery work?

Non-fatal errors trigger recovery phrases automatically. The caller may never notice. Fatal errors execute a graceful shutdown with a hangup hook that captures final state for debugging.

Is observability included in the $0.16/min price?

Yes. Per-component latency, barge-in analytics, error taxonomy, and the call_timeline feed are all included. No separate APM subscription required.

Trusted by 2,000+ Companies

Debug in Minutes, Not Hours.

Build on a platform where every component is instrumented and every event is correlated. No third-party APM required.