NVIDIA Unveils New Framework for Secure Voice-Powered AI Agents with Multimodal RAG

NVIDIA has introduced a comprehensive guide for developers to build production-grade voice agents using the newly released Nemotron model family. These agents move beyond simple API calls by integrating real-time speech recognition with multimodal retrieval-augmented generation. The system allows for conversational AI that is grounded in enterprise data and protected by multilingual safety guardrails. The core of the architecture relies on six specialized models working in a directed graph. The Nemotron Speech ASR handles ultra-low latency transcription, feeding text directly into the retrieval pipeline as it arrives. This streaming capability ensures the interaction feels natural and responsive rather than a series of disjointed queries. Data grounding is achieved through the llama-nemotron-embed-vl model, which can process text and images simultaneously. This allows the agent to index complex technical diagrams and manuals without extra preprocessing steps. A dedicated reranking step further sharpens accuracy by assessing relevance using both visual and textual context. Reasoning is powered by the Nemotron-3 Nano model, which features a hybrid Mamba-Transformer architecture. This design supports a massive 1 million token context window, allowing the agent to synthesize information from long documents and user history in a single request. An optional thinking mode is also available for complex logic tasks. Safety is integrated as a distinct microservice via the llama-3.1-nemotron-safety-guard. This model monitors for harmful content and personally identifiable information across more than 20 languages and cultural contexts. By separating safety logic, enterprises can update guardrails independently of the core reasoning engine. Developers can start building locally with a single 24GB VRAM GPU before scaling to production. The workflow utilizes LangGraph for orchestration and can be deployed via NVIDIA NIM microservices or DGX Spark. This approach aims to streamline the transition from experimental prototype to enterprise-scale assistant. More details are available in the GitHub companion notebook.

NVIDIA has released a comprehensive blueprint for developers to build advanced voice-powered agents that ground their responses in enterprise data while maintaining strict safety standards. Revealed alongside a suite of new Nemotron models at CES 2026, the framework addresses the technical challenges of stitching together speech recognition, retrieval, and reasoning. This end-to-end approach allows creators to move beyond basic prototypes into production-ready systems that can interact naturally with users. The core of the system relies on the Nemotron Speech ASR model, which provides ultra-low latency audio transcription by maintaining a cache of encoder states. This innovation significantly reduces the compute costs typically associated with streaming audio while keeping response times fast enough for real-time conversation. By combining this with the Nemotron-3 Nano model, the agent can reason over a massive one-million-token context window to provide highly accurate and contextual answers. Retrieval is handled through a multimodal pipeline that uses new embedding and reranking models to process both text and images natively. Developers can now index slide decks, technical charts, and scanned documents without needing a separate OCR layer, as the llama-nemotron-embed-vl-1b-v2 model handles these inputs directly. This results in a 6 to 7 percent gain in accuracy when retrieving relevant information from complex, visually heavy datasets.

To ensure reliability in commercial settings, NVIDIA integrated a multilingual safety guardrail that filters both user queries and agent responses across 20 languages. The safety model detects harmful content and personally identifiable information while accounting for cultural nuances and informal speech patterns. This layer acts as a critical filter for the agentic workflow, preventing the system from generating unsafe or non-compliant output during live voice sessions.

The entire architecture is orchestrated via LangGraph, which structures the agent as a directed graph to ensure clean handoffs between transcription, retrieval, and generation. This design allows for a local-first development environment that scales seamlessly to NVIDIA NIM microservices for production deployment. Developers can follow the complete step-by-step process in the official NVIDIA notebook to begin testing these capabilities on their own hardware.