Traditional RAG typically retrieves relevant text from a vector database and supplies it to an LLM as context. Automation ...
A VB Pulse survey of 101 enterprises finds 57% traced a wrong AI agent answer to bad context, and only 25% have a governed context layer in production.
As AI systems evolve from assistants into autonomous collaborators, enterprises will need durable memory, explicit semantics, lineage, governance, and explainability. AllegroGraph and GraphTalker ...
Arango believes recognition highlights its native multimodel architecture, customer adoption, and contextual data foundation ...
10d
Modern Data & Knowledge Platforms: The Foundation Every AI Strategy Actually Runs On: SD Times 100
Part of the SD Times 100 2026 series. See the full SD Times 100 2026 list for every category and honoree. Every conversation ...
Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?
NUS researchers' MRAgent framework reduces LLM agent memory retrieval to 118K tokens per query — vs. 3.26M for LangMem — ...
Artificial Intelligence (AI) agents based on Retrieval-Augmented Generation (RAG) technology are rapidly proliferating. RAG ...
GraphRAG explains why AI is shifting from isolated text to connected knowledge, and what that means for AI search optimization. Making your brand machine-readable and increasing its chances of being ...
Dr Christian Kunz and Marwan Ezzat of Bär & Karrer argue that as AI tools converge, technical literacy, governance, and data ...
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