Modern conversational AI is no longer limited to simple request-response interactions. Today's AI assistants stream tokens in real time, process voice conversations with minimal latency, invoke external tools, and coordinate with other services to deliver intelligent experiences.

In this talk, we'll dive into the communication protocols and architectural patterns that make real-time conversational AI possible. Starting with Server-Sent Events (SSE) for token streaming, we'll explore when to use WebSockets for bidirectional communication, how WebRTC enables voice-based AI interactions, and how emerging standards such as Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication are shaping the next generation of AI systems.

Using Python and modern frameworks such as FastAPI and asyncio, we'll examine practical implementation patterns, discuss trade-offs between different protocols, and explore how event-driven architectures can be used to build scalable AI applications.

Attendees will learn:
• How token streaming works in modern LLM applications
• When to choose SSE, WebSockets, WebRTC, or gRPC
• How MCP enables AI agents to interact with tools and external systems
• How agent-to-agent communication enables collaborative AI workflows
• Best practices for building low-latency conversational AI systems in Python
• Real-world architecture patterns for production-scale AI applications