A comprehensive reference design and Python implementation for building real-time AI agents using Azure OpenAI or AI Foundry. This repository provides architecture patterns, best practices, and production-ready code for implementing conversational AI agents with real-time streaming capabilities.
This project demonstrates how to build scalable, real-time multi-agent solutions that can:
- Handle streaming responses for immediate user feedback in latency-sensitive environments (call center, customer service, etc.)
- Support multi-modal inputs and outputs (text, voice, and images) using the latest AI models from Azure OpenAI or AI Foundry
- Provide a comprehensive observability framework to cover classical application monitoring and agent-specific monitoring requirements
- Ensure security and compliance by default with data handling best practices
- Scale for production use cases
- Provide a user-friendly interface for managing agent definitions, configurations, and states at scale
- Real-Time Streaming: Implements real-time streaming of multi-modal inputs and responses to enhance user experience in latency-sensitive applications.
- Multi-Agent Architecture: Supports multiple agents working collaboratively to handle complex tasks and workflows.
- Observability: Includes logging, monitoring, and tracing capabilities to ensure system reliability and performance across all agents.
- Security and Compliance: Ensures data handling best practices are followed by default.
- Scalability: Designed to scale for production use cases.
- User-Friendly Interface: Provides an intuitive interface for managing agent definitions, configurations, and states at scale.
Comprehensive documentation is available to help you get started and make the most of the Real-Time Agent project. Visit the Documentation folder for more information.