Real-time voice agents for inbound calls
In short
Voice Agents are real-time phone and web agents that pick up inbound calls, reason with an LLM and speak back with text-to-speech, or run as one speech-to-speech model instead. They reach callers over WebRTC in the browser, SIP on real phone numbers, or a WebSocket for your own telephony backend. Every agent can call tools, hand off to a person, and gets tested against simulated callers scored by an LLM judge before it takes a real call.
What Voice Agents does
- Three ways in
- Answer inbound calls over SIP on numbers you bring, WebRTC from a browser or app, or a WebSocket from your own backend. Dynamiq has no outbound dialer.
- Knowledge during the call
- Search your knowledge bases mid-call, so answers come from your own policies and documents while the caller waits.
- A curated model catalog
- 15 voice model vendors for speech-to-text, language models, text-to-speech and speech-to-speech, each with its measured latency and cost.
- Per-turn latency
- Every call shows a waterfall of transcription, end-of-turn detection, LLM time to first token and speech synthesis, and turn-taking and key-term settings tune how the agent listens.
- Tools and transfers
- Give the agent HTTP tools, client tools your app answers, MCP servers and labeled transfer destinations to hand a caller to a person. Callers can key in digits, and the agent can send tones to work through another system's phone menu.
- Simulations, not guesswork
- Run scripted callers against the deployed agent and have an LLM judge score pass rate, accuracy and experience before real callers do.
A curated model catalog, measured
Every stage of a pipeline agent, speech-to-text, the language model and text-to-speech, and every realtime speech-to-speech model, comes from a catalog Dynamiq has wired up and measured, so you pick from combinations known to work rather than assembling one yourself. An estimate bar above the fields shows the configuration's cost per minute and latency before you deploy, so a choice is a comparison, not a guess. Across every stage, Dynamiq's catalog spans 15 voice model vendors.
- The default combination, Cartesia, OpenAI and ElevenLabs, is chosen for latency, not for any single benchmark
- Speech-to-text models with native end-of-turn detection remove a whole pipeline stage instead of speeding one up
- Bring your own OpenAI-compatible LLM endpoint, or a custom voice ID from your own provider account
Where the milliseconds go
A caller judges a voice agent mostly on timing. On a pipeline agent, Dynamiq measures four stages on every turn: end-of-turn detection, transcription, LLM time to first token and text-to-speech time to first byte, and shows them as a proportional bar so the stage to fix is visible at a glance. Picking a speech-to-text model with native end-of-turn detection, such as Deepgram's flux models or Cartesia's ink-2, removes a whole stage instead of speeding one up, which is usually the biggest single latency win available.
- Turn sensitivity and end-of-speech silence tune how eagerly the agent replies
- Realtime, speech-to-speech agents report time to first token instead of a stage breakdown
- Test calls and voice-mode simulations are the only way to get real numbers
I need to move my appointment to next week.
transcribed +110 ms, end of turn +80 ms
I can do Tuesday at 10 or Thursday at 2. Which works?
LLM time to first token, TTS time to first byte, 0.9 s end-to-end
Thursday, please.
transcribed +95 ms, end of turn +70 ms
Done. You will get a text confirmation shortly.
LLM time to first token, TTS time to first byte, 0.8 s end-to-end
Tested before it takes a real call
Simulations run a set of scripted callers, defined by a persona, a goal and an expected outcome, against the deployed agent and have an LLM judge decide whether it did what you said it should. Run them as text for fast, cheap iteration on reasoning and tool use, or as voice to also exercise speech recognition and synthesis and get real latency percentiles. Turn a call that went wrong into a scenario with one click from the Calls tab, so every regression becomes a permanent test.
- A run scores scenarios passed, accuracy and experience, with a judge rationale per scenario
- Generate starter scenarios from the agent's own configuration, or write them by hand
- Simulations run against the deployed agent, so results reflect production behavior
Caller persona and goal
A patient who can only come after 4 pm and wants the earliest slot this week.
Expected outcome
The agent offers a slot after 4 pm and confirms it without transferring the call.
Voice model vendors
- Deepgram
- AssemblyAI
- Cartesia
- ElevenLabs
- Speechmatics
- OpenAI
- Groq
- Baseten
- Kokoro (via DeepInfra)
- xAI
- Anthropic
- Cerebras
- Fireworks AI
- Together AI
Where teams use it
- Contact center voice agentsAnswer your phone lines with voice agents that look up the caller's account, resolve routine calls and transfer to a person when a call needs one.
- Customer serviceAnswer inbound calls and chats, look up and act on the account, and hand complex cases to a person, with every conversation traced.
- Insurance claims processingTake first notice of loss by phone or chat, read the documents, check coverage and prepare the claim, with an adjuster deciding every payment.
Works with
- Agent BuilderDesign an agent on a visual canvas or in the open-source Python SDK. Both compile to the same engine, so what you build ships either way.
- EvalsScore an agent on a dataset before you ship, then keep scoring a sample of its live traffic after you deploy.
- ObservabilityEvery run is traced and replayable, node by node, with the cost, latency and tokens each step used.
Questions and answers
Can Dynamiq Voice Agents make outbound calls?
No. Voice Agents answer inbound calls only, whether they arrive over the phone, the web or your own backend. There is no outbound dialing API.
How do callers reach a voice agent?
Over WebRTC from a browser or mobile app, over SIP from a phone number you connect through your own provider such as Twilio or Telnyx, or over a WebSocket from a backend that already holds the audio, such as a telephony stack.
How is a voice agent tested before it goes live?
You can talk to it yourself from the Test tab, and you can run simulations: scripted callers with a persona and an expected outcome, scored by an LLM judge for pass rate, accuracy and experience. Voice-mode simulations also produce real latency numbers.
Do configuration changes need a redeploy?
No. The worker reads the agent's current instructions, models, tools and settings at the start of every call, so an edit applies to the next call immediately. You only redeploy to pick up a new platform worker version.
What happens when the agent cannot help a caller?
If you configure transfer destinations, the agent can hand the caller to a person with a cold transfer, announcing the handoff first. Without any destinations configured, it cannot transfer and says so instead of failing silently.
See an agent on your own workflow.
Bring a process and its documents. Our engineers will show you how Dynamiq runs it, in your environment or ours.