venturebeat
With 91% accuracy, open source Hindsight agentic memory provides 20/20 vision for AI agents stuck on failing RAG

It has become increasingly clear in 2025 that retrieval augmented generation (RAG) isn't enough to meet the growing data requirements for agentic AI.RAG emerged in the last couple of years to become the default approach for connecting LLMs to external knowledge. The pattern is straightforward: chunk documents, embed them into vectors, store them in a database, and retrieve the most similar passages when queries arrive. This works adequately for one-off questions over static documents. But the architecture breaks down when AI agents need to operate across multiple sessions, maintain context over time, or distinguish what they've observed from what they believe.A new open source memory architecture called Hindsight tackles this challenge by organizing AI agent memory into four sepa [...]

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venturebeat
Six data shifts that will shape enterprise AI in 2026

For decades the data landscape was relatively static. Relational databases (hello, Oracle!) were the default and dominated, organizing information into familiar columns and rows.That stability eroded [...]

Match Score: 201.36

venturebeat
Databricks' Instructed Retriever beats traditional RAG data retrieval by 70% — enterprise metadata was the missing link

A core element of any data retrieval operation is the use of a component known as a retriever. Its job is to retrieve the relevant content for a given query. In the AI era, retrievers have been used a [...]

Match Score: 159.05

venturebeat
'Observational memory' cuts AI agent costs 10x and outscores RAG on long-context benchmarks

RAG isn't always fast enough or intelligent enough for modern agentic AI workflows. As teams move from short-lived chatbots to long-running, tool-heavy agents embedded in production systems, thos [...]

Match Score: 150.73

venturebeat
DeepSeek’s conditional memory fixes silent LLM waste: GPU cycles lost to static lookups

When an enterprise LLM retrieves a product name, technical specification, or standard contract clause, it's using expensive GPU computation designed for complex reasoning — just to access stati [...]

Match Score: 140.62

venturebeat
Nvidia launches enterprise AI agent platform with Adobe, Salesforce, SAP among 17 adopters at GTC 2026

Jensen Huang walked onto the GTC stage Monday wearing his trademark leather jacket and carrying, as it turned out, the blueprints for a new kind of monopoly.The Nvidia CEO unveiled the Agent Toolkit, [...]

Match Score: 135.17

venturebeat
GAM takes aim at “context rot”: A dual-agent memory architecture that outperforms long-context LLMs

For all their superhuman power, today’s AI models suffer from a surprisingly human flaw: They forget. Give an AI assistant a sprawling conversation, a multi-step reasoning task or a project spanning [...]

Match Score: 118.08

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Databricks research shows multi-step agents consistently outperform single-turn RAG when answers span databases and documents

Data teams building AI agents keep running into the same failure mode. Questions that require joining structured data with unstructured content, sales figures alongside customer reviews or citation co [...]

Match Score: 114.76

venturebeat
Nvidia's agentic AI stack is the first major platform to ship with security at launch, but governance gaps remain

For the first time on a major AI platform release, security shipped at launch — not bolted on 18 months later. At Nvidia GTC this week, five security vendors announced protection for Nvidia's a [...]

Match Score: 113.30

venturebeat
How xMemory cuts token costs and context bloat in AI agents

Standard RAG pipelines break when enterprises try to use them for long-term, multi-session LLM agent deployments. This is a critical limitation as demand for persistent AI assistants grows.xMemory, a [...]

Match Score: 110.53