A multilingual CMS operating model for editorial, subscriptions, and audience automation
ClientMIT Technology Review
MIT Technology Review runs a global editorial operation with licensed international editions across Latin America, Spain, and Asia. Each edition maintains its own editorial voice, its own audience, and its own publishing cadence but shares a common source of record: the English-language master archive out of Cambridge.
The problem they asked us to solve was deceptively operational. Translating and republishing hundreds of articles a week across regional sites involved a long chain of manual handoffs: source discovery, translation briefing, fact-check re-verification, image rights re-licensing, CMS entry, SEO normalization, newsletter assembly, and social distribution. Each edition had built its own patchwork of freelance vendors and custom scripts. The cost was high, the latency was higher, and the editorial team spent more time coordinating than editing.
What we built
We designed an agent-enabled CMS operating model that sits behind the publishing stack rather than replacing it. Five specialized agents collaborate across the pipeline:
- A Source Agent monitors the Cambridge archive, scores newly published pieces against each edition's editorial brief, and queues candidates for regional editors to greenlight.
- A Translation Agent produces first-pass translations into six target languages using a glossary trained on ten years of published TR material, and flags idiomatic risks for human review.
- A Rights Agent re-verifies image and illustration licenses for each regional territory and substitutes assets where rights do not carry over.
- A Publishing Agent handles CMS entry, structured metadata, SEO normalization, and publication scheduling against each edition's cadence calendar.
- A Distribution Agent assembles newsletters, prepares social copy variants per channel, and coordinates send windows across time zones.
How the oversight works
Every agent action lands in a unified editorial review queue. Regional editors-in-chief see each piece at three checkpoints candidacy, translation, and pre-publish with one-click approve, edit, or reject. Rejections are fed back into the translation and sourcing models as training signal. No piece is published without a human sign-off, but the human is intervening at the level of editorial judgment, not mechanical production.
What it produced
Over 280 articles per week now move through the pipeline across six language editions. Time-to-publish dropped 85%, with most pieces landing on regional sites within 36 hours of the Cambridge master. Newsletter open rates improved in every market a side effect of send-time optimization the distribution agent runs against each audience. The editorial team reclaimed roughly 60% of the hours they previously spent on production coordination, which they redirected to commissioning original regional reporting.
“We didn't want agents doing journalism. We wanted agents running the freight forwarding so our editors could do more journalism.”Editorial Operations, MIT Technology Review
What we're tuning now
The current iteration is focused on extending the model to audio and video formats, and on giving each edition its own fine-tuned translation memory so house style is preserved without manual post-editing. The engagement is in its third year.
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first agent.
30 minutes with our team. We’ll walk through your workflow, the failure modes we’d expect, and the shape of a 6-week pilot.