MIT Technology Review
filed
1h
Connecting AI agents to enterprise knowledge
read it at the source — MIT Technology Review →
“For all the data that AI systems continually amass and analyze, enterprise AI agents often suffer from a curious shortcoming: a lack of knowledge. More than data, knowledge is the understanding of what the data means in the context of individual organizations. AI agents need this understanding to reason about situations, make decisions, and ultimately…”
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Three kinds of line, three kinds of source. Every measurement is computed from primary artifacts we read ourselves — Hugging Face model cards and file listings, GitHub release feeds, OpenRouter's model catalogue. Every headline comes from the publisher's own feed, linked and attributed; no aggregator sits in between, and nothing is rewritten. Where a publisher syndicates a short summary in that same feed, it is shown under the headline the way a headline is — translated on the Korean page with the published original kept underneath it. Only the summary field is ever shown. The field that carries the article is read and never shown: it screens out 'summaries' that are really the article's opening lines, and — for a publisher's own announcements only — it is the evidence the event pages' What-happened notes are written and checked against. One exception, since 2026-09-03: the page a discussion thread links to is fetched and read once, to write a single sentence saying what it claims, labelled as the post's own claim; nothing from it is quoted. Every discussion is a public thread, linked, with its top comments in the order that thread ranked them and in the words they were written — selected by it, not by us.
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