Presented by DigitalOceanFrom refactoring codebases to debugging production code, AI agents are already proving their value. But scaling them in production remains the exception, not the rule. In DigitalOcean’s 2026 Currents research report, based on a survey of more than 1,100 developers, CTOs, and founders, 67% of organizations using agents report productivity gains. Meanwhile, 60% of respondents say applications and agents represent the greatest long-term value in the AI stack. Yet, only 10% are scaling agents in production. The top blocker? Forty-nine percent cite the high cost of inference. It's not just the price of a single API call. It's the compounding cost as agents chain tasks and run autonomously. Nearly half of respondents now spend 76–100% of their AI budget on [...]
The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use inference-tim [...]
Researchers at the University of Illinois Urbana-Champaign and Google Cloud AI Research have developed a framework that enables large language model (LLM) agents to organize their experiences into a m [...]
Microsoft today announced the general availability of Agent 365 and Microsoft 365 Enterprise 7, two products designed to bring security and governance to the rapidly growing population of AI agents op [...]
Two days after releasing what analysts call the most powerful open-source AI model ever created, researchers from China's Moonshot AI logged onto Reddit to face a restless audience. The Beijing-b [...]
Artificial intelligence agents powered by the world's most advanced language models routinely fail to complete even straightforward professional tasks on their own, according to groundbreaking re [...]
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, [...]
In a new paper that studies tool-use in large language model (LLM) agents, researchers at Google and UC Santa Barbara have developed a framework that enables agents to make more efficient use of tool [...]