OpenAI’s new Workspace Agents can automate tasks across Slack, Salesforce, and other enterprise tools, eliminating manual handoffs and running 24/7 with persistent memory. Here’s what changes—and how to get started.
Testing AI agents in live environments without risking user experience requires a strategic approach. Shadow deployments let new models process real traffic in parallel, revealing performance gaps before full rollout.
Fragmented AI agents fail to collaborate, creating bottlenecks in automation. A new startup’s middleware promises to unify them into a seamless, scalable workforce.
A new open-source tool replaces complex databases with a lightweight, Git-backed wiki that lets AI agents share and grow knowledge across sessions without losing context.
OpenClaw challenges the chatbot status quo by treating AI as an execution engine—not just a text generator—redefining how agents interact with systems. Discover why its modular skill system and human oversight gap matter for the future of AI tools.
Business teams using AI agents for workflows like Slack or calendar management often hit the same hurdle: prompts aren’t enough. Discover how harness engineering adapts to non-coding use cases using Markdown and persistent instruction files.
Traditional AI agents reset with every session, erasing your hard-earned preferences and lessons. New systems break this cycle by building memory, refining skills and curating history automatically—transforming fleeting interactions into lasting intelligence.
Xiaomi has launched two open-source AI models designed to outperform proprietary rivals on task-completion efficiency, slashing token usage by up to 60%. With a 310B-parameter architecture and a 1M-token context window, they’re reshaping enterprise AI economics for developers worldwide.
IBM’s new AI coding platform targets enterprise adoption by blending automated agents with mandatory human checkpoints, saving teams up to 70% time on selected tasks.