agents That See and Speak: Building voice multimodal AI with realtime APIs, vision, speech pipelines

Build Voice and Multimodal AI Agents Engineered for Production RealityVoice AI has crossed the demo-to-production threshold. Realtime speech-to-speech models, sub-second time-to-first-audio, and mature telephony middleware now make phone-grade conversational agents achievable for any serious engineering team. Agents That See and Speak is your comprehensive, end-to-end guide to designing, building, and deploying production-grade voice and vision AI systems that scale.What you will learn inside: - Latency Budgets: Build a concrete latency budget targeting sub-800ms time-to-first-audio using off-the-shelf components.- Architectures: When to choose realtime speech-to-speech APIs (like OpenAI Realtime and Gemini Live) versus cascaded STT-LLM-TTS pipelines.- Turn-Taking & Barge-In: Implement robust endpointing, Voice Activity Detection (VAD), and barge-in algorithms that make conversational flows feel human.- Multimodal Systems: Integrate camera feeds, live screen shares, and video streams into the realtime conversational audio loop.- Telephony Engineering: Connect your agents to the public phone network using SIP, PSTN, Twilio, Telnyx, LiveKit, and Pipecat.- On-Device Voice: Run local voice agents utilizing Apple Neural Engine, Google Tensor, and Qualcomm NPUs.- Evaluation & Safety: Learn LLM-as-judge, automated latency metrics, and adversarial red-teaming.Packed with concrete latency benchmarks, real-world APIs (Deepgram, ElevenLabs, Vapi, Retell), and production-tested architectures, this guide is written specifically for AI engineers, product developers, and conversational AI specialists who want to ship voice agents that work, not just demo well.