OpenMatter Network Introduces Verifiable Trust Layer for Secure Collaboration and AI Agents

Computer scientist Phillip Isola cuts through the hype to explain how AI agents work and what the future might hold for this rapidly advancing technology.

Amazon's AI push could redefine enterprise operations, boosting AI adoption and impacting sectors like finance and crypto with new efficiencies. The post Amazon launches $1B FDE organization to deploy AI agents across enterprise clients appeared first on Crypto Briefing.

The launch gives enterprises broader access to building domain-specific, autonomous AI agents.

OKX launched a beta marketplace for the agentic economy, enabling AI agents to autonomously find work and collaborate with other agents.

With AI agents increasingly expected to remember conversations, preferences, and decisions over extended periods, Microsoft Research has developed Memora, a memory system designed to provide more scalable and reliable long-term recall than existing approaches. AI agents are increasingly expected to retain context across weeks or months rather than individual chat sessions. Memory can become fragmented, leading to duplicate information and slower retrieval as knowledge grows. According to Microsoft, Memora can solve this problem by decoupling what the AI remembers from how it looks up that information, ultimately reducing context token usage by up to 98% while matching or exceeding full-context accuracy, Microsoft Research claimed in a blog post. Limitations of today’s memory architectures As AI assistants and autonomous agents move into long-horizon deployments, the absence of a principled memory system has become a critical bottleneck. While modern LLMs are powerful reasoners, they st

With AI agents increasingly expected to remember conversations, preferences, and decisions over extended periods, Microsoft Research has developed Memora, a memory system designed to provide more scalable and reliable long-term recall than existing approaches. AI agents are increasingly expected to retain context across weeks or months rather than individual chat sessions. Memory can become fragmented, leading to duplicate information and slower retrieval as knowledge grows. According to Microsoft, Memora can solve this problem by decoupling what the AI remembers from how it looks up that information, ultimately reducing context token usage by up to 98% while matching or exceeding full-context accuracy, Microsoft Research claimed in a blog post. Limitations of today’s memory architectures As AI assistants and autonomous agents move into long-horizon deployments, the absence of a principled memory system has become a critical bottleneck. While modern LLMs are powerful reasoners, they s

There are a variety of security concerns about artificial intelligence (AI), especially when it comes to the behavior of agentic AI. But until recently, the concept of locking down the models to prevent tampering hasn’t gotten a lot of attention. Now, a security technology called “confidential computing” has emerged that could help solve that problem: it protects AI models from hackers by restricting models to authorized users. (It also protects data wherever it is — in storage, when moving between systems, and when it is accessed.) With many top cloud and hardware providers championing confidential computing for AI, Computerworld talked with Dion Harris, Nvidia’s senior director of high-performance computing and AI factory solutions, about what the technology does and how it works. width="1024" height="722" sizes="auto, (max-width: 1024px) 100vw, 1024px"> Dion Harris, Nvidia’s senior director of high-performance computing and AI factory solutions. Nvidia Why should organizations

Whether you are using an AI code generator, vibe coding, or applying spec-driven development methodologies, your job doesn’t end with AI writing the code. Whether you’re using AI to develop applications, APIs, data pipelines, AI agents, or other automations, writing the code is just one part of the job. Developers must still perform code validation, test applications, automate deployment, and configure infrastructure. According to one survey, only 16% of a developer’s time is spent writing code. The remaining 84% is spent on other activities including defining requirements, triaging bugs, and addressing vulnerabilities. Additionally, while AI code generation speeds up development, it can come at the cost of quality and collaboration. In Atlassian’s State of Teams 2026 survey, nearly 50% of respondents say their AI outputs aren’t reliably high quality and admit that using AI is a compromise between speed and quality. Knowledge workers say the pressure to execute is also problematic, wit