InfoWorld

Taming the generative AI back end

Writing code that interacts with LLM services requires bridging two different worlds. Use these tips and techniques to bind ...
Design And Reuse

Dnotitia Open-Sources AKB on GitHub, an Agent-Native Knowledge Infrastructure for Enterprise AI

Goes beyond traditional RAG knowledge bases by accumulating conversations, work records, decision context, and outputs generated by AI agents Converts scattered documents, files, databases, and ...

VIDEO INTERVIEW: Progress Software’s John Ainsworth on agentic RAG, MCP and why bypassing business logic is "fraught with incredible risk"

You can't spell Ainsworth without AI. It's a small linguistic coincidence, but it's also a neat shorthand for the conversation I had with John Ainsworth, Executive ...

Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production

The standard architecture — chunking documents, embedding them into a vector database, and retrieving top-k results via ...

The RAG era is ending for agentic AI — a new compilation-stage knowledge layer is what comes next

Vector database pioneer Pinecone recognizes this and is pivoting to meet the specific needs of agentic AI. The company today announced Nexus, which it positions as a knowledge eng ...

AI Agent Reportedly Deletes Company’s Entire Database, Admits to Violating Guardrails

A Cursor AI agent deleted a company’s entire production database, ignoring instructions prohibiting it from running destructive commands.
Hacker

Production RAG Is Not a Vector Database: A Practical Blueprint for Retrieval You Can Trust

I lead work across partnerships, business development and data, with a focus on strategy, impact and decision making.
Microsoft

Advancing agentic AI with Microsoft databases across a unified data estate

This week, we are excited to kick off SQLCon 2026 alongside FabCon in Atlanta. Bringing these SQL and Fabric communities together creates a unique opportunity to learn, connect, and share what’s next ...
IEEE

To RAG or Not to RAG, That Is the Question: Effective Text-to-SQL Generation Under Ambiguity

Abstract: Natural language interfaces to databases promise to democratize data access, yet current Text-to-SQL systems fail to address a fundamental challenge: user questions are often ambiguous.