Building Conversational AI with WebRTC and LLMs: Real-Time Voice Agents
A technical guide to building real-time voice AI agents using WebRTC for audio transport, speech-to-text, LLM reasoning, and text-to-speech in a low-latency pipeline.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
A technical guide to building real-time voice AI agents using WebRTC for audio transport, speech-to-text, LLM reasoning, and text-to-speech in a low-latency pipeline.
How to build responsive AI applications using streaming, WebSockets, and SSE, with practical patterns for token streaming, agent status updates, and real-time collaboration.
Where Claude's agentic stack is heading — shared skill ecosystems, standard composition, governance — and the hygiene to prepare for it without over-betting.
The real metrics for Claude Agent Skills — autonomy rate, rework, escalation quality, tokens per task, and eval pass rate — that prove agentic AI works.
An end-to-end walkthrough of building a Claude refund agent with Skills, MCP, subagents, and projects — from messy problem to safely shipped automation.
Failure scenarios, blast radius, and containment for Claude Agent Skills — scoping, approval gates, sandboxing, and staged rollout for safe agentic AI.
What engineers, integrators, and leaders must learn for Claude Agent Skills to work in production — and the new roles that emerge around them.
How to scale Claude Agent Skills from one team to the whole org without chaos — catalog, tiering, federated ownership, and failure modes to avoid.
An honest decision guide for choosing Claude Agent Skills versus prompts, MCP servers, Projects, or subagents — and when not to build a skill.