venturebeat
On-device AI agents hit a hard memory limit. Apple's new architecture routes around it.

On-device AI models have stayed small because the entire weight set has to live in DRAM, capping practical parameter counts well below what server-side deployments use. Enterprise architects evaluating agentic workloads have had to choose between capable cloud-dependent models and limited on-device ones. Apple's third-generation foundation models, announced at WWDC26, break that constraint by moving the weight set off DRAM entirely.The AFM 3 family was developed in collaboration with Google and spans five models: two on-device and three server-based, all running within Apple's Private Cloud Compute boundary. The server-side models, including AFM 3 Cloud Pro for agentic tool use and complex reasoning, run on Nvidia GPUs in Google Cloud. The on-device architecture is Apple's o [...]

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venturebeat
A 0.12% parameter add-on gives AI agents the working memory RAG can't

AI agents forget. Every time a coding assistant loses track of a debugging thread, or a data analysis agent re-ingests the same context it already processed, the team pays in latency, token costs, and [...]

Match Score: 89.77

venturebeat
Apple’s new Siri AI is more than just a smarter assistant — it's a new enterprise app layer

Apple’s new Siri AI, unveiled yesterday at Apple's annual Worldwide Developers Conference (WWDC 2026), may look like a consumer product story on the surface. But for enterprise developers and I [...]

Match Score: 86.80

venturebeat
Google PM open-sources Always On Memory Agent, ditching vector databases for LLM-driven persistent memory

Google senior AI product manager Shubham Saboo has turned one of the thorniest problems in agent design into an open-source engineering exercise: persistent memory.This week, he published an open-sour [...]

Match Score: 84.58

venturebeat
MeMo's memory model lets teams upgrade their LLM without retraining it — and performance jumps 26%

Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits.MeMo, a [...]

Match Score: 82.57

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: 80.18

venturebeat
MIT's MeMo lets teams swap in a better LLM without retraining — and performance jumps 26%

Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits.MeMo, a [...]

Match Score: 78.64

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: 78.53

venturebeat
OpenAI unveils Workspace Agents, a successor to custom GPTs for enterprises that can plug directly into Slack, Salesforce and more

OpenAI introduced a new paradigm and product today that is likely to have huge implications for enterprises seeking to adopt and control fleets of AI agent workers.Called "Workspace Agents," [...]

Match Score: 77.16

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: 75.12