Simon Willison's Weblog·3 months ago·
51
● 11 sources
Ethan Mollick's guide to choosing AI tools has shifted focus from chat models like ChatGPT and Claude toward agentic systems capable of performing hours of human work autonomously. The most powerful implementations give AI access to your computer through desktop apps using modes like ChatGPT's Codex and Claude's Cowork, which enable broader capabilities than browser-based versions. As agentic AI matures, the choice of tool increasingly depends on whether users need simple conversation or autonomous work completion, with naming conventions across platforms remaining confusing.
MIT Technology Review·3 months ago·
33
● 3 sources
Intel conducted thousands of experiments on agentic AI workloads in enterprise environments and identified five practical lessons for running software agents that automate business tasks across systems. The company extended Terminal-Bench, an open-source benchmarking tool, to measure agent performance beyond just language model inference, using deterministic record-replay of LLM responses across a task mix including compilation, database operations, and machine learning training. Enterprises should plan capacity using agent density per vCPU rather than agent count, monitor task latency instead of average CPU utilization, and scale out systems by default to support production workloads on established business processes like code creation and ticket triaging.
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