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When JPMorgan Asset Management reported that AI spending accounted for two-thirds of US GDP growth in the first half of 2025, it wasn’t just a statistic – it was a signal. Enterprise leaders are making trillion-dollar bets on AI transformation, even as market observers debate whether we might be witnessing bubble-era exuberance. The conversation reached…
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A stealth artificial intelligence startup founded by an MIT researcher emerged this morning with an ambitious claim: its new AI model can control computers better than systems built by OpenAI and Anthropic — at a fraction of the cost. OpenAGI, led by chief executive Zengyi Qin, released Lux, a foundation model designed to operate computers…
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Hybrid cloud security was built before the current era of automated, machine-based cyberattacks that take just milliseconds to execute and minutes to deliver devastating impacts to infrastructure. The architectures and tech stacks every enterprise depends on, from batch-based detection to siloed tools to 15-minute response windows, stood a better chance of defending against attackers moving…
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Enterprises are investing billions of dollars in AI agents and infrastructure to transform business processes. However, we are seeing limited success in real-world applications, often due to the inability of agents to truly understand business data, policies and processes. While we manage the integrations well with technologies like API management, model context protocol (MCP) and…
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As AI systems enter production, reliability and governance can’t depend on wishful thinking. Here’s how observability turns large language models (LLMs) into auditable, trustworthy enterprise systems. Why observability secures the future of enterprise AI The enterprise race to deploy LLM systems mirrors the early days of cloud adoption. Executives love the promise; compliance demands accountability;…
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Researchers at the University of Science and Technology of China have developed a new reinforcement learning (RL) framework that helps train large language models (LLMs) for complex agentic tasks beyond well-defined problems such as math and coding. Their framework, Agent-R1, is compatible with popular RL algorithms and shows considerable improvement on reasoning tasks that require…
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Agent memory remains a problem that enterprises want to fix, as agents forget some instructions or conversations the longer they run. Anthropic believes it has solved this issue for its Claude Agent SDK, developing a two-fold solution that allows an agent to work across different context windows. “The core challenge of long-running agents is that…
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Researchers at Alibaba’s Tongyi Lab have developed a new framework for self-evolving agents that create their own training data by exploring their application environments. The framework, AgentEvolver, uses the knowledge and reasoning capabilities of large language models for autonomous learning, addressing the high costs and manual effort typically required to gather task-specific datasets. Experiments show…
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Hello, dear readers. Happy belated Thanksgiving and Black Friday! This year has felt like living inside a permanent DevDay. Every week, some lab drops a new model, a new agent framework, or a new “this changes everything” demo. It’s overwhelming. But it’s also the first year I’ve felt like AI is finally diversifying — not…
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If you asked most enterprise leaders which AI tools are delivering ROI, many would point to front-end chatbots or customer support automation. That’s the wrong door. The most value-generating AI systems today aren’t loud, customer-facing marvels. They’re tucked away in backend operations. They work silently, flagging irregularities in real-time, automating risk reviews, mapping data lineage,…