Agentic AI systems are autonomous platforms that perceive, reason, act, and learn to achieve complex goals with minimal human intervention. This guide explains how they differ from generative AI, their core components, orchestration patterns, and enterprise governance considerations.

Companies seeing the highest returns from AI are making deliberate investments tied to specific business outcomes, grounded in clean and governed data. This article outlines 10 proven AI business solutions and the conditions necessary for success.

Retrieval Augmented Generation (RAG) connects large language models to external knowledge bases through a five-stage pipeline — ingestion, embedding, retrieval, augmentation, and generation — enabling accurate, domain-specific answers without retraining the model. A production RAG workflow requires selecting the right embedding model, configuring vector database indexing and chunking strategies, and implementing hybrid search that combines semantic vector search with keyword fallback to maximize retrieval quality. RAG evaluation must measure retrieval precision and generation faithfulness independently, because strong LLM performance cannot compensate for a weak information retrieval component, and continuous data updates are essential to prevent stale knowledge from degrading response ac…

NVIDIA technology runs 81% of the TOP500 and 90% of the systems new to the list. 26 systems on the TOP500 adopted the NVIDIA Grace CPU, up eight from the previous list. The top eight systems on the Green500 run on NVIDIA GPUs and nine of the top 10 use NVIDIA technologies. No. 1 on the Green500, KAIROS, uses a single NVIDIA Grace Hopper Superchip. 376 of the TOP500 systems are interconnected using NVIDIA networking. A record 35 NVIDIA AI HPC supercomputers are in development across Europe.

Vector search is a search technique that finds results based on meaning and context rather than exact keyword matches. It uses embeddings to identify similar text, images, audio, and other content, solving limitations of keyword-only search. It is used in RAG, enterprise search, recommendations, and anomaly detection. Production systems often combine vector and keyword search for better results, and managed services like Databricks AI Search add reranking, metadata filtering, and governance.

This article compares data lakes and cloud data warehouses, highlighting that data lakes store raw, multi-format data for machine learning and advanced analytics, while cloud data warehouses optimize high-concurrency SQL performance for structured reporting. Data lakehouses, built on open table formats like Delta Lake, unify both advantages and are projected to become the dominant architecture for enterprise analytics.

Telecom operators have seen remarkable returns from generative AI in network management, customer care, and back-office automation. The industry is now moving toward autonomous networks where AI agents proactively monitor and coordinate changes. At TM Forum’s DTW Ignite 2026, NVIDIA and partners showcase building blocks including synthetic data, domain models, secure runtimes, and simulation, enabling more resilient networks and richer AI-driven services.