---
title: "Synthetic Monitoring for Voice Agents: Checkly, Datadog, and Building Your Own"
description: "Real users generate noise. Synthetic checks generate signal. Here's how to run a fake voice call against your agent every minute and catch regressions before customers do."
canonical: https://callsphere.ai/blog/vw3c-synthetic-monitoring-voice-agents-checkly-datadog
category: "AI Infrastructure"
tags: ["Synthetic Monitoring", "Checkly", "Datadog", "Voice AI"]
author: "CallSphere Team"
published: 2026-04-12T00:00:00.000Z
updated: 2026-05-07T09:59:38.172Z
---

# Synthetic Monitoring for Voice Agents: Checkly, Datadog, and Building Your Own

> Real users generate noise. Synthetic checks generate signal. Here's how to run a fake voice call against your agent every minute and catch regressions before customers do.

> **TL;DR** — Real-traffic SLOs detect regressions late. A 1-minute synthetic call detects them in 60 seconds. Combine both.

## What goes wrong

```mermaid
flowchart LR
  Twilio["Twilio Media Streams"] -- "WS · μlaw 8kHz" --> Bridge["FastAPI Bridge :8084"]
  Bridge -- "PCM16 24kHz" --> OAI["OpenAI Realtime"]
  OAI --> Bridge
  Bridge --> Twilio
  Bridge --> Logs[(structured logs · OTel)]
```

CallSphere reference architecture

Synthetic monitoring is well-understood for HTTP — Datadog Synthetics and Checkly let you run a Playwright script every minute and alert on failure. The same idea applied to voice is rarer, because nobody ships an "audio Playwright." A real synthetic voice check has to: place a phone call (or open a WebRTC peer), play a pre-recorded utterance, score the agent's response, and report metrics.

Without it, your first signal of a regression is a real customer call — at which point the bad experience is already shipped.

## How to monitor

A synthetic voice check should test:

1. **Connect path** — phone number rings, call is answered, audio negotiates.
2. **First-token latency** — how long until the agent speaks back.
3. **Intent match** — does the agent's first reply match the expected intent for the test utterance.
4. **Transactional path** — can the agent complete a known booking/transfer flow.
5. **Cost** — do not exceed N tokens or M cents per check.

Run one synthetic per vertical every minute. Run a longer transactional check every 15 minutes. Page on three consecutive failures.

## CallSphere stack

CallSphere built its own synthetic harness because off-the-shelf doesn't do voice well in 2026. Architecture:

- **Caller bot** in Go using Pion WebRTC and a pre-recorded Opus utterance.
- **STT scoring** via Deepgram (cheap and fast for synthetic).
- **Intent classifier** via gpt-4o-mini judging "did the response match expected intent."
- **Result** posted to a Postgres `synthetic_results` table; metrics scraped by Prometheus.

We run six synthetics every minute (one per vertical) plus three transactional flows every 15 minutes:

- **Healthcare FastAPI `:8084`** — synthetic calls 555-0100, says "I need to verify my insurance," expects intent `insurance_verification`.
- **Real Estate** — synthetic asks "do you have a 3-bedroom listing in Austin?" expects intent `property_search` and a successful tool call to the listings DB.
- **Sales** — synthetic plays the pricing question; checks that the agent quotes $149 / $499 / $1499 from [/pricing](/pricing).
- **After-hours Bull/Redis queue** — synthetic schedules a callback and verifies the queued job exists.

Costs: ~$3.20/day per vertical for STT + gpt-4o-mini judging. Cheap enough to run forever.

We expose the synthetic dashboard publicly at status.callsphere.ai. $1499 enterprise tier gets per-tenant synthetics. Try the [14-day trial](/trial).

## Implementation

1. **Caller bot in Go** opening a WebRTC peer to your edge.

```go
pc, _ := webrtc.NewPeerConnection(cfg)
audioTrack, _ := webrtc.NewTrackLocalStaticSample(...)
pc.AddTrack(audioTrack)
go playOpus(audioTrack, "fixtures/insurance_q.opus")
```

1. **Capture agent audio**, hand to Deepgram, score:

```python
text = deepgram.transcribe(agent_audio)
verdict = openai.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role":"user", "content": f"Does this response answer 'insurance verification'? Reply yes or no.\n\n{text}"}],
)
```

1. **Persist + alert.**

```sql
INSERT INTO synthetic_results (vertical, ftl_ms, intent_ok, ts)
VALUES ('healthcare', 720, true, NOW());
```

1. **Alertmanager** alerts on 3 consecutive failures or FTL p95 > 1200ms.
2. **Replay on regression.** Every failed synthetic auto-creates a Linear ticket with the audio and the trace.

## FAQ

**Q: Can I use Datadog Synthetics for voice?**
A: Their browser test can hit a WebRTC page; not a clean fit for SIP/PSTN. We use Datadog Synthetics for our HTTP APIs and homemade for voice.

**Q: How realistic should the test utterance be?**
A: Use real recorded voices, not TTS — TTS hits the model differently and gives misleadingly high scores.

**Q: Won't synthetics inflate my OpenAI bill?**
A: We see ~$0.15/check on gpt-4o-realtime. Six verticals × 1440 checks/day = ~$1300/mo across all. Worth it.

**Q: How do I keep synthetics out of business metrics?**
A: Tag every synthetic call with `x-synthetic: true` on the SIP INVITE; filter from analytics rollups.

**Q: What about Checkly?**
A: Great for HTTP/Playwright API checks (we use it for our `/api/admin/*` routes). Not voice.

## Sources

- [Datadog — Synthetic Testing and Monitoring](https://docs.datadoghq.com/synthetics/)
- [Checkly — Datadog alternative](https://www.checklyhq.com/datadog-alternative/)
- [Alert24 — Checkly alternatives 2026](https://alert24.net/blog/checkly-alternatives)
- [Nurbak — Synthetic monitoring tools 2026](https://nurbak.com/en/blog/synthetic-monitoring-tools/)

---

Source: https://callsphere.ai/blog/vw3c-synthetic-monitoring-voice-agents-checkly-datadog