Watching an AI-policy question in real time: how a Watching Agents by Inithouse agent builds hypotheses and scores evidence
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At Inithouse, a studio running parallel product experiments, we built Watching Agents to answer a question we kept asking ourselves: what happens to a fast-moving topic while nobody is watching? Across over a hundred deployed agents, we observed that most questions worth tracking shift meaningfully within the first two weeks. Here is how one agent tracks an AI-policy question from start to score change. The setup: one question, five minutes You type a question about the future. Not a search…
1Key Takeaways
- At Inithouse, a studio running parallel product experiments, we built Watching Agents to answer a question we kept asking ourselves: what happens to a fast-moving topic while nobody is watching?
- Across over a hundred deployed agents, we observed that most questions worth tracking shift meaningfully within the first two weeks.
- Here is how one agent tracks an AI-policy question from start to score change.
- The setup: one question, five minutes You type a question about the future.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that at Inithouse, a studio running parallel product experiments, we built Watching Agents to answer a question we kept asking ourselves: what happens to a fast-moving topic while nobody is watching?
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