TypeScript for Agentic AI: Building Type-Safe Agent Systems
Build type-safe agentic AI with TypeScript — typed tool definitions, Zod schemas for structured output, type-safe handoffs, and generic agent classes.
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.
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Build type-safe agentic AI with TypeScript — typed tool definitions, Zod schemas for structured output, type-safe handoffs, and generic agent classes.
Master voice activity detection algorithms, turn-taking strategies, overlapping speech handling, and silence threshold tuning to build natural-sounding conversational AI agents.
Reduce voice agent latency to sub-second response times by optimizing STT, LLM inference, TTS pipelines, using streaming, caching, and predictive techniques.
Learn how to build a comprehensive testing and QA pipeline for voice agents, covering audio simulation, accuracy measurement, regression testing, and production monitoring.
Build a comprehensive testing and QA pipeline for voice agents covering audio simulation, STT accuracy measurement, TTS quality evaluation, end-to-end conversation testing, and regression monitoring.
Add function tools to voice agents for booking appointments, searching databases, processing payments, and executing real-time actions with audio feedback during tool execution.
Build low-latency browser-based voice agents using WebRTC peer connections and OpenAI's Realtime API — from obtaining ephemeral tokens to establishing audio tracks and handling speech-to-speech interactions.
Enable WebSocket transport in the OpenAI Agents SDK for persistent connections, reduced latency, and faster multi-turn agent interactions using set_default_openai_responses_transport.
How to design APIs for AI agent platforms — comparing REST, GraphQL, and gRPC for agent invocation, streaming responses, tool registration, and multi-agent orchestration.