How to Add Workflow Orchestration to Your Voice AI Stack

1. The Problem

Real-time voice frameworks—like Vapi, ElevenLabs, OpenAI Realtime, Retell, or Deepgram—make it easy to build conversational agents that listen, reason, and respond instantly.

But after the call ends, you still face all the non-real-time work:

• Routing the result to the right team or system
• Managing human approvals or escalations
• Logging decisions for compliance
• Keeping process state across multiple calls or systems

Those workflows require persistence, coordination, and auditability—capabilities that most voice frameworks don’t provide.

That’s where WorkflowGen, a workflow engine, fits in.

2. Architecture Overview

WorkflowGen acts as the post-call orchestrator for your voice agents. It handles what happens after the conversation ends—automating system updates, assigning human tasks, and maintaining process state.

‍Typical flow:

1. The voice agent collects structured output (intent, data, confidence).

2. The agent sends the JSON payload to WorkflowGen via REST or GraphQL API.

3. WorkflowGen interprets the data and triggers post-call actions:

• Human review if confidence is low
• CRM or ERP updates
• Notifications or escalations

4. WorkflowGen logs every step and decision for audit and analytics.

3. Implementation Steps

1. Expose the voice agent’s output

‍Configure your voice platform to send final conversation summaries or intents to an HTTPS endpoint.

2. Create a process in WorkflowGen‍

Model a process with:

• Input variables (customer_id, intent, confidence, reason)
• One automatic step for API calls
• One human task for manual review

3. Integrate via REST
The agent posts its structured payload to:

POST https://<your_server>/wfgen/hooks/<your_hooks_url_key>
Content-Type: application/json

{
  "operation": "createRequest",
  "args": {
    "input": {
      "processName": "<your_process_name>",
      "processVersion": 1
    }
  }
}

‍

4. Map fields to WorkflowGen variables
‍Match JSON keys to process fields using WorkflowGen’s data mapping interface or GraphQL mutation.

5. Handle asynchronous calls (optional)
For long or chained voice interactions, configure WorkflowGen incoming webhooks so your agent can signal completion later.

6. Test end-to-end
Simulate a call, send output, and watch WorkflowGen assign or automate follow-up actions.

4. Real Use Case Example

Voice-Driven Customer Support Escalation‍

1. Customer calls a support AI built on Vapi.
2. AI handles basic questions and classifies the issue.
3. For uncertain cases, it sends the result to WorkflowGen.
4. WorkflowGen assigns a human agent for review.
5. Once the human closes the case, WorkflowGen updates the CRM and logs the entire workflow.

Result: every call outcome is tracked, reviewed, and auditable.

5. Developer Benefits

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6. Advanced Pattern: Multi-Stage Voice Workflows

For complex pipelines—like multi-day onboarding or compliance verification—you can:

• Chain multiple voice sessions in a single WorkflowGen instance.
• Add waiting states for callbacks or document uploads.
• Trigger new calls from WorkflowGen via external APIs.

That transforms real-time voice agents into components of long-running, auditable enterprise workflows.

7. Takeaway

Voice frameworks manage conversation. WorkflowGen manages process.

‍By adding WorkflowGen to your voice AI stack, you gain persistence, auditability, and human oversight without losing real-time performance.

‍Develop once. Deploy safely. Use your existing voice platform—and let WorkflowGen handle everything after the call.