AI voice agents have crossed into production viability. VAPI, Google Dialogflow CX, and LiveKit all support SIP — the same protocol Genesys Cloud uses for voice. That means you can connect an AI agent directly to your Genesys Cloud contact center and have it handle real calls, with a warm transfer to a human agent when needed. Here's the architecture.
Why SIP is the right integration layer
Every AI voice platform eventually exposes a SIP interface because SIP is the lingua franca of telephony. It's how calls move between systems. Rather than integrating AI agents via API (which means intercepting and proxying audio streams, with all the latency that implies), SIP lets you treat the AI agent as another endpoint in your call routing graph — just like a physical phone or a softphone client.
From Genesys Cloud's perspective, a SIP-connected AI agent looks like a SIP endpoint. Architect flows can transfer calls to it, receive transfers back from it, and handle the call after the AI completes its interaction. The integration point is clean and the call audio quality is native — no web RTC translation layers, no quality degradation.
The Genesys Cloud side: Architect flow design
The Genesys Cloud configuration is straightforward. The AI agent receives calls via a Blind Transfer or a Consultative Transfer action in an Architect flow. The key decision is how to pass context.
For a blind transfer, context travels as SIP headers. You can set custom SIP headers in Architect using the “Set SIP Headers” action before the transfer — passing the customer's ANI, account number (if you've already done a data action lookup), the queue they were destined for, or any other variable you've collected in the flow. The receiving AI platform needs to read these headers and use them to personalize the conversation.
For a warm transfer back to a human agent, the AI platform initiates a SIP REFER or a new outbound call to a Genesys Cloud DID, optionally passing conversation context back via SIP headers or a URL to a context record. The Architect flow at the receiving end can read those headers and route accordingly — landing the call in the right queue, with screen pop data pre-loaded.
VAPI
VAPI is purpose-built for AI voice agents and has the most straightforward SIP configuration of the three platforms. To connect VAPI to Genesys Cloud:
- In VAPI, create a SIP trunk under Settings → Phone Numbers → Import SIP Trunk. VAPI will give you a SIP URI in the format
sip:yourorg@sip.vapi.ai. - In Genesys Cloud, add a SIP trunk pointing to VAPI's SIP endpoint. Use a BYOC Premises or BYOC Cloud trunk configuration depending on whether you're routing through your own SBC first.
- Create a DID in VAPI associated with the assistant you want to invoke for inbound calls. For Genesys-originated transfers, VAPI uses the called number (To header) to determine which assistant handles the call.
- In your Architect flow, use a Transfer to External action with the VAPI SIP URI as the target, and set any context SIP headers before the transfer action.
VAPI's assistant configuration handles the LLM (you choose: GPT-4o, Claude, Gemini), STT (Deepgram is the default), and TTS (ElevenLabs, PlayHT, or others). Latency is typically 800–1200ms end-to-end for the AI response turn, which is on the edge of acceptable for most use cases. VAPI has been improving this steadily.
VAPI also exposes a webhook for call events — you can log every turn of the AI conversation and the final outcome back to your data infrastructure, keeping Genesys Cloud as the system of record for the overall call while VAPI handles the AI interaction detail.
Google Dialogflow CX
Dialogflow CX integrates with Genesys Cloud via two paths: the Genesys native Dialogflow bot integration (which runs inside Architect flows for digital channels and voice) and a direct SIP integration via Dialogflow CX's Telephony Gateway or CCAI Platform.
The native Genesys integration is the easier path for most use cases — Architect calls the Dialogflow CX agent via the built-in bot action, the bot handles the conversation turn, and control returns to Architect when the bot's flow terminates. This works well for structured IVR-style interactions where the bot collects specific inputs (intent, account number, issue type) and hands off.
The SIP path via CCAI Platform (Google's Contact Center AI product) is more powerful but significantly more complex. CCAI Platform acts as a full telephony intermediary — calls route to Google's infrastructure, Dialogflow CX handles the conversation, and CCAI Platform manages the call lifecycle including transfers back to your Genesys queues. This gives you Google's STT/TTS quality (which is excellent) and full CCAI feature access, but it effectively moves your call routing control plane to Google for any calls that enter the AI flow.
For pure Genesys-integrated deployments, the native bot integration is the right choice. For organizations that want to use Google's telephony infrastructure as a primary layer with Genesys as an agent desktop and queue manager, CCAI Platform makes sense.
LiveKit
LiveKit is an open-source real-time communication framework (WebRTC-based) that has added SIP support and a purpose-built Agents framework for AI voice applications. The key differentiator is that you can self-host the entire stack — your calls don't touch a third-party AI voice platform's infrastructure.
The LiveKit SIP integration with Genesys Cloud works via LiveKit's SIP server, which you deploy on your own infrastructure. Configuration steps:
- Deploy LiveKit server and the LiveKit SIP bridge (both available as Docker images or Kubernetes Helm charts).
- Configure a SIP trunk in Genesys Cloud pointing to your LiveKit SIP bridge endpoint. Assign a DID for inbound calls from Genesys.
- Build your AI agent using the LiveKit Agents Python SDK. The framework handles STT (via Deepgram, AssemblyAI, or Whisper), LLM (Claude, GPT-4o, or any API-compatible model), and TTS (ElevenLabs, Cartesia, or others) with a pipeline abstraction that manages turn detection and interruption handling.
- The agent runs as a LiveKit participant in a room. When a SIP call arrives, LiveKit dispatches an agent instance to handle it.
The self-hosted nature means your audio never leaves your infrastructure — a requirement for certain regulated industries (healthcare, financial services). The tradeoff is operational overhead: you're running and scaling the LiveKit infrastructure yourself. LiveKit Cloud (their managed offering) removes this burden but reintroduces third-party audio routing.
LiveKit's latency characteristics depend heavily on your STT and LLM choices. With streaming STT (Deepgram Nova-2) and a small fast model (Claude Haiku, GPT-4o mini), response latency can get to 600–800ms — better than VAPI for straightforward interactions.
Use cases that work well today
Not every call is a good AI voice candidate. The patterns that work in production:
- After-hours handling: AI agent takes the call after business hours, collects issue type and contact info, creates a callback record. No attempt to resolve — just capture and acknowledge. High success rate, low complexity.
- Overflow handling: When queue depth exceeds a threshold, Architect routes overflow to an AI agent that offers a callback or handles simple inquiries. The AI resolves what it can; anything complex gets a callback scheduled.
- Authenticated self-service: Post-authentication (handled in Architect via a data action lookup), transfer to an AI agent with customer context pre-loaded. The AI handles account inquiries, status lookups, simple transactions.
- First-level triage and routing:AI agent replaces a DTMF IVR menu with a conversational interaction. “What can I help you with today?” — intent classification routes to the right queue with context attached.
What to build for before you start
The most common failure mode in AI voice deployments is underestimating the edge case surface. The demo works great. Production traffic exposes: heavy accents the STT misrecognizes, customers who don't follow conversational norms, background noise on mobile calls, customers who ask questions outside the agent's defined scope.
Before going live, define explicit handoff conditions — the set of signals that trigger an immediate transfer to a human. Include: detected frustration (multiple rephrasing attempts, raised voice via sentiment detection), explicit human request (“let me talk to someone”), topics outside the agent's knowledge scope (LLM should be instructed to escalate rather than hallucinate), and a session time limit.
A graceful, fast handoff to a human with context is far better than an AI agent that overstays its welcome. Design the exit conditions first.