A new review paper argues that the real bottleneck for autonomous AI agents isn't the language model itself but the software layer wrapped around it. Tools, memory, testing, and permission boundaries turn a stateless model into a working agent. Deepseek is already building a dedicated "Harness" team in Beijing with a core formula that confirms the thesis: model plus harness equals AI agent.<br /> The article New review paper argues code is how AI agents think and act, not just what they produce appeared first on The Decoder. [...]
reMarkable knows you’d like to use its e-paper tablet on the go, but the size of its current products don’t make that easy. To address this, it’s launching a smaller, pocket-sized version of its [...]
Attackers stole a long-lived npm access token belonging to the lead maintainer of axios, the most popular HTTP client library in JavaScript, and used it to publish two poisoned versions that install a [...]
Anthropic on Monday released Code Review, a multi-agent code review system built into Claude Code that dispatches teams of AI agents to scrutinize every pull request for bugs that human reviewers rout [...]
Web infrastructure giant Cloudlflare is seeking to transform the way enterprises deploy AI agents with the open beta release of Dynamic Workers, a new lightweight, isolate-based sandboxing system that [...]
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," [...]
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 [...]
As enterprise codebases grow, AI agents tasked with analyzing them are buckling under the weight of long-horizon tasks that require multiple interactions and tool calls. Dividing the work among a team [...]