The Future of AI: Structured Vibe Coding - An Improved Approach to AI Software Development
In this post from The Future of AI series, the author introduces structured vibe coding, a method for managing AI agents like a software team using specs, GitHub issues, and pull requests. By applying this approach with GitHub Copilot, they automated a repetitive task—answering Microsoft Excel-based questionnaires—while demonstrating how AI can enhance developer workflows without replacing human oversight. The result is a scalable, collaborative model for AI-assisted software development.The Future of AI: Vibe Code with Adaptive Custom Translation
This blog explores how vibe coding—a conversational, flow-based development approach—was used to build the AdaptCT playground in Azure AI Foundry. It walks through setting up a productive coding environment with GitHub Copilot in Visual Studio Code, configuring the Copilot agent, and building a translation playground using Adaptive Custom Translation (AdaptCT). The post includes real-world code examples, architectural insights, and advanced UI patterns. It also highlights how AdaptCT fine-tunes LLM outputs using domain-specific reference sentence pairs, enabling more accurate and context-aware translations. The blog concludes with best practices for vibe coding teams and a forward-looking view of AI-augmented development paradigms.
Building Enterprise Voice-Enabled AI Agents with Azure Voice Live API
The sample application covered in this post demonstrates two approaches in an end-to-end solution that includes product search, order management, automated shipment creation, intelligent analytics, and comprehensive business intelligence through Microsoft Fabric integration. Use Case Scenario: Retail Fashion Agent Core Business Capabilities: Product Discovery and Ordering: Natural language product search across fashion categories (Winter wear, Active wear, etc.) and order placement. REST APIs hosted in Azure Function Apps provide this functionality and a Swagger definition is configured in the Application for tool action. Automated Fulfillment: Integration with Azure Logic Apps for shipment creation in Azure SQL Database Policy Support: Vector-powered QnA for returns, payment issues, and customer policies. Azure AI Search & File Search capabilities are used for this requirement. Conversation Analytics: AI-powered analysis using GPT-4o for sentiment scoring and performance evaluation. The Application captures the entire conversation between the customer and Agent and sends them to an Agent running in Azure Logic Apps to perform call quality assessment, before storing the results in Azure CosmosDB. When during the voice call the customer indicates that the conversation can be concluded, the Agent autonomously sends the conversation history to the Azure Logic App to perform quality assessment. Advanced Analytics Pipeline: Real-time Data Mirroring: Automatic synchronization from Azure Cosmos DB to Microsoft Fabric OneLake Business Intelligence: Custom Data Agents in Fabric for trend analysis and insights Executive Dashboards: Power BI reports for comprehensive performance monitoring Technical Architecture Overview The solution presents two approaches, each optimized for different enterprise scenarios: 🎯Approach 1: Direct Model Integration with GPT-Realtime Architecture Components This approach provides direct integration with Azure Voice Live API using GPT-Realtime model for immediate speech-to-speech conversational experiences without intermediate text processing. The Application connects to the Voice Live API uses a Web socket connection. The semantics of this API are similar to the one used when connecting to the GPT-Realtime API directly. The Voice Live API provides additional configurability, like the choice of a custom Voice from Azure Speech Services, options for echo cancellation, noise reduction and plugging an Avatar integration. Core Technical Stack: GPT-Realtime Model: Direct audio-to-audio processing Azure Speech Voice: High-quality TTS synthesis (en-IN-AartiIndicNeural) WebSocket Communication: Real-time bidirectional audio streaming Voice Activity Detection: Server-side VAD for natural conversation flow Client-Side Function Calling: Full control over tool execution logic Key Session Configuration The Direct Model Integration uses the session configuration below: session_config = { "input_audio_sampling_rate": 24000, "instructions": system_instructions, "turn_detection": { "type": "server_vad", "threshold": 0.5, "prefix_padding_ms": 300, "silence_duration_ms": 500, }, "tools": tools_list, "tool_choice": "auto", "input_audio_noise_reduction": {"type": "azure_deep_noise_suppression"}, "input_audio_echo_cancellation": {"type": "server_echo_cancellation"}, "voice": { "name": "en-IN-AartiIndicNeural", "type": "azure-standard", "temperature": 0.8, }, "input_audio_transcription": {"model": "whisper-1"}, } Configuration Highlights: 24kHz Audio Sampling: High-quality audio processing for natural speech Server VAD: Optimized threshold (0.5) with 300ms padding for natural conversation flow Azure Deep Noise Suppression: Advanced noise reduction for clear audio Indic Voice Support: en-IN-AartiIndicNeural for localized customer experience Whisper-1 Transcription: Accurate speech recognition for conversation logging Connecting to the Azure Voice Live API The voicelive_modelclient.py demonstrates advanced WebSocket handling for real-time audio streaming: def get_websocket_url(self, access_token: str) -> str: """Generate WebSocket URL for Voice Live API.""" azure_ws_endpoint = endpoint.rstrip("/").replace("https://", "wss://") return ( f"{azure_ws_endpoint}/voice-live/realtime?api-version={api_version}" f"&model={model_name}" f"&agent-access-token={access_token}" ) async def connect(self): if self.is_connected(): # raise Exception("Already connected") self.log("Already connected") # Get access token access_token = self.get_azure_token() # Build WebSocket URL and headers ws_url = self.get_websocket_url(access_token) self.ws = await websockets.connect( ws_url, additional_headers={ "Authorization": f"Bearer {self.get_azure_token()}", "x-ms-client-request-id": str(uuid.uuid4()), }, ) print(f"Connected to Azure Voice Live API....") asyncio.create_task(self.receive()) await self.update_session() Function Calling Implementation The Direct Model Integration provides client-side function execution with complete control: tools_list = [ { "type": "function", "name": "perform_search_based_qna", "description": "call this function to respond to the user query on Contoso retail policies, procedures and general QnA", "parameters": { "type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"], }, }, { "type": "function", "name": "create_delivery_order", "description": "call this function to create a delivery order based on order id and destination location", "parameters": { "type": "object", "properties": { "order_id": {"type": "string"}, "destination": {"type": "string"}, }, "required": ["order_id", "destination"], }, }, { "type": "function", "name": "perform_call_log_analysis", "description": "call this function to analyze call log based on input call log conversation text", "parameters": { "type": "object", "properties": { "call_log": {"type": "string"}, }, "required": ["call_log"], }, }, { "type": "function", "name": "search_products_by_category", "description": "call this function to search for products by category", "parameters": { "type": "object", "properties": { "category": {"type": "string"}, }, "required": ["category"], }, }, { "type": "function", "name": "order_products", "description": "call this function to order products by product id and quantity", "parameters": { "type": "object", "properties": { "product_id": {"type": "string"}, "quantity": {"type": "integer"}, }, "required": ["product_id", "quantity"], }, } ] 🤖 Approach 2: Azure AI Foundry Agent Integration Architecture Components This approach leverages existing Azure AI Foundry Service Agents, providing enterprise-grade voice capabilities as a clean wrapper over pre-configured agents. It does not entail any code changes to the Agent itself to voice enable it. Core Technical Stack: Azure Fast Transcript: Advanced multi-language speech-to-text processing Azure AI Foundry Agent: Pre-configured Agent with autonomous capabilities GPT-4o-mini Model: Agent-configured model for text processing Neural Voice Synthesis: Indic language optimized TTS Semantic VAD: Azure semantic voice activity detection Session Configuration The Agent Integration approach uses advanced semantic voice activity detection: session_config = { "input_audio_sampling_rate": 24000, "turn_detection": { "type": "azure_semantic_vad", "threshold": 0.3, "prefix_padding_ms": 200, "silence_duration_ms": 200, "remove_filler_words": False, "end_of_utterance_detection": { "model": "semantic_detection_v1", "threshold": 0.01, "timeout": 2, }, }, "input_audio_noise_reduction": {"type": "azure_deep_noise_suppression"}, "input_audio_echo_cancellation": {"type": "server_echo_cancellation"}, "voice": { "name": "en-IN-AartiIndicNeural", "type": "azure-standard", "temperature": 0.8, }, "input_audio_transcription": {"model": "azure-speech", "language": "en-IN, hi-IN"}, } Key Differentiators: Semantic VAD: Intelligent voice activity detection with utterance prediction Multi-language Support: Azure Speech with en-IN and hi-IN language support End-of-Utterance Detection: AI-powered conversation turn management Filler Word Handling: Configurable processing of conversational fillers Agent Integration Code The voicelive_client.py demonstrates seamless integration with Azure AI Foundry Agents. Notice that we need to provide the Azure AI Foundry Project Name and an ID of the Agent in it. We do not need to pass the model's name here, since the Agent is already configured with one. def get_websocket_url(self, access_token: str) -> str: """Generate WebSocket URL for Voice Live API.""" azure_ws_endpoint = endpoint.rstrip("/").replace("https://", "wss://") return ( f"{azure_ws_endpoint}/voice-live/realtime?api-version={api_version}" f"&agent-project-name={project_name}&agent-id={agent_id}" f"&agent-access-token={access_token}" ) async def connect(self): """Connects the client using a WS Connection to the Realtime API.""" if self.is_connected(): # raise Exception("Already connected") self.log("Already connected") # Get access token access_token = self.get_azure_token() # Build WebSocket URL and headers ws_url = self.get_websocket_url(access_token) self.ws = await websockets.connect( ws_url, additional_headers={ "Authorization": f"Bearer {self.get_azure_token()}", "x-ms-client-request-id": str(uuid.uuid4()), }, ) print(f"Connected to Azure Voice Live API....") asyncio.create_task(self.receive()) await self.update_session() Advanced Analytics Pipeline GPT-4o Powered Call Analysis The solution implements conversation analytics using Azure Logic Apps with GPT-4o: { "functions": [ { "name": "evaluate_call_log", "description": "Evaluate call log for Contoso Retail customer service call", "parameters": { "properties": { "call_reason": { "description": "Categorized call reason from 50+ predefined scenarios", "type": "string" }, "customer_satisfaction": { "description": "Overall satisfaction assessment", "type": "string" }, "customer_sentiment": { "description": "Emotional tone analysis", "type": "string" }, "call_rating": { "description": "Numerical rating (1-5 scale)", "type": "number" }, "call_rating_justification": { "description": "Detailed reasoning for rating", "type": "string" } } } } ] } Microsoft Fabric Integration The analytics pipeline extends into Microsoft Fabric for enterprise business intelligence: Fabric Integration Features: Real-time Data Mirroring: Cosmos DB to OneLake synchronization Custom Data Agents: Business-specific analytics agents in Fabric Copilot Integration: Natural language business intelligence queries Power BI Dashboards: Interactive reports and executive summaries Artefacts for reference The source code of the solution is available in the GitHub Repo here. An article on this topic is published on LinkedIn here A video recording of the demonstration of this App is available below: Part1 - walkthrough of the Agent configuration in Azure AI Foundry - here Part2 - demonstration of the Application that integrates with the Azure Voice Live API - here Part 3 - demonstration of the Microsoft Fabric Integration, Data Agents, Copilot in Fabric and Power BI for insights and analysis - here Conclusion Azure Voice Live API enables enterprises to build sophisticated voice-enabled AI assistants using two distinct architectural approaches. The Direct Model Integration provides ultra-low latency for real-time applications, while the Azure AI Foundry Agent Integration offers enterprise-grade governance and autonomous operation. Both approaches deliver the same comprehensive business capabilities: Natural voice interactions with advanced VAD and noise suppression Complete retail workflow automation from inquiry to fulfillment AI-powered conversation analytics with sentiment scoring Enterprise business intelligence through Microsoft Fabric integration The choice between approaches depends on your specific requirements: Choose Direct Model Integration for custom function calling and minimal latency Choose Azure AI Foundry Agent Integration for enterprise governance and existing investments
Announcing the Text PII August preview model release in Azure AI language
Azure AI Language is excited to announce a new preview model release for the PII (Personally Identifiable Information) redaction service, which includes support for more entities and languages, addressing customer-sourced scenarios and international use cases. What’s New | Updated Model 2025-08-01-preview Tier 1 language support for DateOfBirth entity: expanding upon the original English-only support earlier this year, we’ve added support for all Tier 1 languages: French, German, Italian, Spanish, Portuguese, Brazilian Portuguese, and Dutch New entity support: SortCode - a financial code used in the UK and Ireland to identify the specific bank and branch where an account is held. Currently we support this in only English. LicensePlateNumber - the standard alphanumeric code for vehicle identification. Note that our current scope does not support a license plate that contains only letters. Currently we support this in only English. AI quality improvements for financial entities, reducing false positives/negatives These updates respond directly to customer feedback and address gaps in entity coverage and language support. The broader language support enables global deployments and the new entity types allow for more comprehensive data extraction for our customers. This ensures an improved service quality for financial, criminal justice, and many other regulatory use cases, enabling more accurate and reliable service for our customers. Get started A more detailed tutorial and overview of the service feature can be found in our public docs. Learn more about these releases and several others enhancing our Azure AI Language offerings on our What’s new page. Explore Azure AI Language and its various capabilities Access full pricing details on the Language Pricing page Find the list of sensitive PII entities supported Try out Azure AI Foundry for a code-free experience We are looking forward to continuously improving our product offerings and features to meet customer needs and are keen to hear any comments and feedback.
