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I tried building AI agents and ended up needing an institution

Building AI agents revealed that real work requires institutional controls—ownership, authorization, review, closure, and replay—not just agent capabilities. The author proposes 'AI institutions' with primitives like WorkRecord and VerifierDecision, tested on a prediction-market desk, and offers five checks to ensure trustworthy AI work.

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I tried building AI agents and ended up needing an institution
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Summary only

Under the source content permission, this page provides an editorial summary, key points, and a link to the original article.

Key points and analysis

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Key points

  • Current AI agents excel at tools and memory but fail at ownership, authority, and closure.
  • The author introduces AI institutional primitives: EmployeePrincipal, RoleContract, WorkRecord, VerifierDecision, etc.
  • A prediction-market desk stress-tested the system, showing improved auditability and governability.
  • Five checks (Ownership, Authority, Evidence, Closure, Replay) quickly assess AI work reliability.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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