Why Voice Agent Teams Need Unified Observability (And How It Complements Datadog)

Voice agent teams typically use 3-5 tools to understand what's happening:

• A testing platform for pre-launch QA
• A voice platform dashboard for call metrics
• Datadog or similar for infrastructure monitoring
• Log aggregators for debugging
• Custom dashboards for business metrics

When something breaks, you switch between tools, correlate timestamps manually, and piece together what happened. This process takes hours when it should take minutes. If you have ever been on-call for a voice system, you know the context-switching tax is real.

This isn't a rip-and-replace of Datadog. It's having native observability that keeps voice-agent-specific data unified—while complementing your existing infrastructure monitoring.

Quick filter: If your incident response needs three tabs and a spreadsheet, you do not have unified observability.

The Problem: Scattered Voice Agent Data

Consider debugging a production voice agent issue:

1. Customer reports problem → Check support tickets
2. Find the call → Voice platform dashboard
3. Listen to audio → Voice platform or separate tool
4. Check agent logs → Log aggregator
5. Review traces → Datadog or Jaeger
6. Compare to test results → Testing platform
7. Identify root cause → Mental correlation across 5+ tools

Each tool switch costs context. Each manual correlation risks missing the connection. A 10-minute issue becomes an hour of investigation.

The root cause is architectural: voice agent data lives in too many places.

Why Voice Agents Need Unified Observability

Voice agent debugging requires correlating data that traditional observability tools don't connect:

The insight you need often spans multiple data types. For example: "This production call failed with the same pattern as a test case that started failing last Tuesday, and the LLM latency spiked at the same time."

Traditional tools can't make this connection because the data lives in different systems.

Native vs. Exported Observability

There are two approaches to voice agent observability:

Approach 1: Export to Existing Tools

Send voice agent data to Datadog, Grafana, or your existing observability stack.

Pros:

• Uses familiar tools
• No new platform to learn

Cons:

• Voice-specific context is lost
• Can't correlate with test results
• Audio playback unavailable
• Speech-level analysis not preserved
• Debugging still requires multiple tools

Approach 2: Native Observability with Complementary Integration

Keep voice-agent-specific data in a purpose-built platform that complements your existing stack.

Pros:

• All voice agent data in one place
• Correlate tests, production calls, and traces
• Audio playback alongside traces
• Speech-level analysis preserved
• Faster debugging

Cons:

• New interface to learn (minimal)
• Additional platform (but unified voice data)

The key insight: you don't need to replace Datadog. General infrastructure monitoring belongs in Datadog. Voice-agent-specific data—tests, calls, evaluations, audio—belongs in a unified voice agent platform.

How Native OpenTelemetry Observability Works

Hamming provides native OpenTelemetry ingestion for voice agent data. Here's what that means:

Trace Ingestion

Send OpenTelemetry traces from your voice agent system to Hamming. Traces show:

• End-to-end call flow
• LLM request/response timing
• Tool call execution
• STT/TTS processing time
• External API calls

Traces appear alongside test results and production call data—in the same interface.

Span Correlation

Each span in a trace correlates with:

• The production call it belongs to
• Similar test scenarios
• Previous occurrences of the same pattern
• Speech-level analysis of that moment in the call

This correlation happens automatically—no manual timestamp matching.

Log Integration

Logs from your voice agent system attach to the relevant call and trace. When debugging, you see logs in context rather than searching a separate system.

Hamming Complements Datadog

Hamming doesn't replace Datadog. The two serve different purposes:

The value of keeping voice agent data unified:

• Debug a production call by listening to audio, reviewing the transcript, seeing the trace, and comparing to test results—all in one view
• Correlate a test failure with a production issue without switching tools
• See speech-level sentiment alongside latency spikes
• Identify patterns across calls that span multiple infrastructure components

What Unified Voice Agent Observability Enables

Faster Incident Response

Before unified observability:

1. Alert fires in Datadog
2. Switch to voice platform to find the call
3. Switch to testing platform to see if scenario was tested
4. Switch back to Datadog for traces
5. Correlate manually
6. Identify root cause (30-60 minutes)

With unified observability:

1. Alert fires with link to call detail
2. See audio, transcript, trace, and test history in one view
3. Identify root cause (5-10 minutes)

Proactive Quality Management

Unified data enables queries you can't run across multiple tools:

• "Show me production calls where latency exceeded our test thresholds"
• "Which test scenarios have started failing since last week's deploy?"
• "What's the correlation between LLM latency and customer sentiment?"
• "Which accent/noise combinations have the highest failure rate?"

Continuous Improvement Loops

When test results, production calls, and traces live together:

• Failed production call → test case with one click (because the data is already there)
• Test failure → similar production calls to understand real-world impact
• Trace anomaly → affected calls to quantify the problem

Technical Implementation

Sending Traces to Hamming

Hamming accepts OpenTelemetry traces via standard protocols:

# Configure your OTel exporter to send to Hamming
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

# Set up the tracer provider
provider = TracerProvider()
processor = BatchSpanProcessor(OTLPSpanExporter(
    endpoint="YOUR_HAMMING_OTEL_ENDPOINT",  # Get from Hamming dashboard
    headers={"authorization": "Bearer YOUR_API_KEY"}
))
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

Correlating Traces with Calls

Add call metadata to your spans for automatic correlation:

tracer = trace.get_tracer(__name__)

with tracer.start_as_current_span("voice_agent_turn") as span:
    span.set_attribute("hamming.call_id", call_id)
    span.set_attribute("hamming.turn_number", turn_number)
    # ... your voice agent logic

Hamming automatically links spans with the production call, enabling unified debugging.

Viewing Unified Data

In the Hamming interface:

• Call detail view shows audio, transcript, evaluation scores, and linked traces
• Trace view shows spans with links to the call and similar test scenarios
• Test results show which production calls match each scenario
• Dashboards combine test, production, and trace metrics

FAQ: Voice Agent Observability

Does Hamming replace our existing observability stack?

No. Hamming complements Datadog and your existing tools. Keep general infrastructure monitoring where it is. Use Hamming for voice-agent-specific data where unified context matters.

What OpenTelemetry protocols does Hamming support?

Hamming supports OTLP (OpenTelemetry Protocol) over gRPC and HTTP. If you're already using OpenTelemetry, you can send traces to Hamming by adding an additional exporter.

Can we send traces to both Datadog and Hamming?

Yes. OpenTelemetry supports multiple exporters. Send traces to Datadog for infrastructure correlation and to Hamming for voice-specific correlation.

How much trace data can Hamming ingest?

Hamming is built for enterprise scale. Contact sales for specific volume limits and pricing for high-volume trace ingestion.

What if we're not using OpenTelemetry yet?

Hamming's native observability still provides value. Test results and production call data are unified without trace integration. Adding OpenTelemetry traces enhances debugging but isn't required.

The Business Case for Unified Observability

Reduced Mean Time to Resolution (MTTR)

Teams using unified voice agent observability report 60-80% reduction in debugging time. Instead of correlating data across 5 tools, everything is in one view.

Better Test Coverage

When you can see which production issues don't have corresponding test cases, you know what to add. The feedback loop from production to testing becomes automatic.

Faster Iteration

Comprehensive visibility into voice agent behavior means faster experimentation. Try a prompt change, see the impact across test and production, and iterate confidently.

Lower Total Cost of Ownership

Five separate tools cost more than one unified platform—in licensing, integration maintenance, and engineering time spent switching contexts.

Getting Started with Unified Observability

Step 1: Connect Your Voice Agent

Use pre-built integrations for Retell, VAPI, LiveKit, ElevenLabs, Pipecat, or Bland. Production calls start flowing to Hamming automatically.

Step 2: Enable Production Monitoring

Turn on production call monitoring. Hamming evaluates every call with 50+ metrics, speech-level sentiment analysis, and automatic tagging.

Step 3: Add OpenTelemetry Traces (Optional)

Configure your voice agent to send OTel traces to Hamming. Traces appear alongside call data with automatic correlation.

Step 4: Unify Your Debugging Workflow

When issues occur, start in Hamming. See the call, hear the audio, review the trace, check related test results—all without switching tools.

The Future of Voice Agent Observability

Voice agents are becoming more complex: multi-agent systems, tool integrations, RAG pipelines, real-time decision making. The debugging challenge will only grow.

Teams that invest in unified observability now will have a significant advantage as complexity increases. They'll debug faster, iterate more confidently, and ship more reliable agents.

Hamming provides native OpenTelemetry observability that complements Datadog and your existing stack. All voice agent data—tests, production calls, traces, evaluations—unified in one platform.

Get started with unified voice agent observability →