Twilio Signal 2025 Keynote Demo
Conversation Relay + Azure Foundry

signal-keynote-demo.mp4

Demo Overview

This demo showcases a multi-agent, voice-based AI system using Twilio ConversationRelay, Azure OpenAI, Azure AI Search, and Azure Foundry. It implements the Talker-Reasoner architecture (paper), a pattern for building agentic voice systems.

• Talker Agent

  • Handles real-time voice interaction with the user
  • Focused on fluency, latency, and dialogue continuity
  • Delegates complex tasks to background processes or back-office agents

• Subconscious Background Processes (Reasoners)

  • Run in parallel without interrupting the user experience
  • Examples in this demo:
    • Vector Memory: Long-term memory retrieval via Azure AI Search (semantic vector index)
    • Summarizer: Monitors user sentiment and topics using lightweight LLM logic

• Back-Office Agents

  • Additional autonomous agents the Talker can query when needed
  • Built using Azure Foundry
  • Handle domain-specific reasoning and slower, computation-heavy tasks
  • Support a modular architecture without burdening the main conversational loop

🖥️ Dual UI

The demo includes a split-screen view of:

• Frontend UI: The user-facing voice interface powered by Twilio
• Backend View: Real-time agent orchestration, including background reasoning, memory queries, and inter-agent messaging

Twilio Setup

1. Create API key and token and set .env file params TWILIO_API_KEY and TWILIO_API_SECRET respectively
2. Create Sync service and set TWILIO_SYNC_SVC_SID in both the .env file and the ui/.env file
3. In the .env.example file, at the end are two mp3 files that need to be uploaded as Twilio assets, and their URLs set respectively
4. Set the DEFAULT_TWILIO_NUMBER to a number from your Twilio account that can make outbound calls
5. Set the DEMO_USER_* variables to your demo user
6. Assign an incoming number (can be the same as outbound)
   1. Set the Webhook to be <YOUR_SERVER>/incoming-call
   2. Set the Call status changes to be <YOUR_SERVER>/call-status

Azure Setup

Most configuration will occur in the Azure AI Foundry or the Azure portal

Azure

Create an Azure account and login to https://portal.azure.com/

Ensure you have an subscription, the transcript storage service uses Azure AI Search which is not available on the free tier.

The following steps are performed in the UI, these can also be completed by commandline or terraform.

1. In the search bar enter AI Search and select the service
2. Create a new service, by pressing the + icon
3. Select your subscription, enter service name e.g. svc-crelay-search, choose deployment region
4. Create a new index, called transcript-store, use the <proj>/server/agents/recall/setup/search-schema.json as the input schema
5. In the .env file set the AZURE_SEARCH_INDEX to the name of the index just created, e.g. transcript-store
6. Also set the AZURE_SEARCH_ENDPOINT url to the Search service just created, can be found under Overview on the side menu
7. Lastly, set the AZURE_ADMIN_KEY, which can be found under Settings > Keys > Primary Admin Key

Azure AI Foundry Configuration

Login to Azure AI Foundry

1. Create a new project and give it an awesome name
2. From the Overview page, copy the API Key on the first page and set the FOUNDRY_API_KEY environment variable
3. Also on the overview page, copy the Azure AI Foundry project endpoint and set the AZURE_CONN_STRING var
4. Deploy the Open AI GPT 4.1 (or similar) model
5. From Azure OpenAI > Copy the Azure OpenAI endpoint and set the AZURE_LLM_ENDPOINT var
6. Set the AZURE_LLM_DEPLOYMENT to gpt-4.1 (or whatever model you deployed), can be found under My assets > Models + Endpoints
7. From the model catalog, deploy the text-embedding-3-large model, set the EMBED_ENDPOINT environment variable

Agent Configuration

Login to Azure AI Foundry

1. Create an agent
2. Copy the agent ID, starting with asst_ and put it in the .env file variable named UNDERWRITER_AGENT_ID
3. Copy or edit the prompt from file <proj>/server/agents/underwriter-agent/instructions.md
4. Add Agent Action. Copy <proj>/server/agents/underwriter-agent/tool-manifest.json into schema of as an OpenAPI 3.0 specified tool Action and name it UnderwriterBrainTool. Note: Replace {HOSTNAME} with your ngrok hostname

Azure CLI (Local Development on OSX)

Ensure Azure CLI is installed

OSX

brew update && brew install azure-cli

Azure - Service User

This will be used in the Fly.io deployment (or other hosting service) The steps below walks you through creating a service user (Service Principal) in Azure and collecting the necessary credentials for automation, scripting, or integration purposes.

✅ Step 1: Register an App in Microsoft Entra ID

1. Go to the Azure Portal
2. Navigate to: Microsoft Entra ID > App registrations
3. Click + New registration
4. Fill in the form:
   • Name: my-service-user
   • Supported account types: Accounts in this organizational directory only (Default)
   • Redirect URI: (Optional) Leave blank unless needed
5. Click Register

📌 Save these values from the Overview page:

• CLIENT_ID = Application (client) ID
• TENANT_ID = Directory (tenant) ID

✅ Step 2: Create a Client Secret

1. After registration, go to Certificates & secrets
2. Click + New client secret
3. Enter a description and choose an expiry (e.g., 6 months, 12 months)
4. Click Add
5. Copy the generated secret value immediately

📌 Save this value:

• CLIENT_SECRET = The generated secret value

⚠️ You will not be able to retrieve the secret again after leaving the page.

✅ Step 3: Assign the Service Principal Access to Azure Resources

1. Go to Subscriptions
2. Select your target Subscription
3. Click Access control (IAM) > + Add > Add role assignment
4. In the Role tab, select a roles Azure AI User and Cognitive Services Contributor
5. In the Members tab:
   • Click + Select members
   • Search for the registered app by name
   • Select it and click Review + assign

✅ Step 4: Get Your Subscription ID

1. Go to Subscriptions
2. Select your active subscription
3. Copy the Subscription ID from the overview pane

📌 Save this value:

• SUBSCRIPTION_ID = The Subscription's GUID

Deploying to Fly.io

1. fly launch to launch a new app with Fly
2. copy all the .env configuration to respective [env] variables in fly.toml
3. copy secrets to .env.flysecrets
4. import the secrets using fly secrets import < ./.env.flysecrets

Populating Demo Data

Once the application has been deployed (or before running locally) ensure that the Twilio Sync objects are populated by running the script: pnpm run data:populate