Building production AI agents by patching together incompatible models for speech, retrieval, and safety creates significant integration challenges. These 'Frankenstein stacks' lead to compounded latency, accuracy degradation between components, and weak, bolt-on security, which are the primary causes of failure in real-world applications, not reasoning errors.
Standard Retrieval-Augmented Generation (RAG) systems often fail because they treat complex documents as pure text, missing crucial context within charts, tables, and layouts. The solution is to use vision language models for embedding and re-ranking, making visual and structural elements directly retrievable and improving accuracy.
While content moderation models are common, true production-grade AI safety requires more. The most valuable asset is not another model, but comprehensive datasets of multi-step agent failures. NVIDIA's release of 11,000 labeled traces of 'sideways' workflows provides the critical data needed to build robust evaluation harnesses and fine-tune truly effective safety layers.