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Open ETF Agent v0.15.2

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Headlines · 3

  1. When an Agent Should Keep Researching or Start Answering

    This tutorial explains how an AI Agent can decide whether more research is needed, whether to answer with qualifications, or whether to stop after hitting execution limits. Using a constructed ETF share-comparison example, it covers evidence gaps, conflicting documents, bounded research, and the separation of verified facts, assumptions, calculations, and judgments. The article also notes that the example is pedagogical rather than a live fund task, while its engineering mechanisms come from the ETF Agent design.

    掘金 · 人工智能 · 🔥 0
  2. ETF Agent Separates Daily Research Generation From Saved Readback

    An engineering article describes an ETF Agent design that separates personalized daily research generation from reading saved results: read-only requests return existing results, while explicit generation requests create or retry research. The design uses a user-and-server-UTC-date key for deduplication, PostgreSQL session-level advisory locks, and a separate limited connection pool for long-held locks so they do not exhaust the pool used by normal queries. It also distinguishes states such as not generated, in progress, successful with no selected items, failed, and saved but unreadable. A source-projection fix allows a card to retrieve the sources explicitly linked to its original research without rewriting the body or rerunning the model. The article also states that the public Open ETF Agent v0.15.2 lacks the account-level daily selection, database generation lock, and scheduled-result integration described in it.

    掘金 · 人工智能 · 🔥 0
  3. How ETF Agent Separates Identity, Authorization, and Resource Isolation

    An ETF Agent engineering article explains how a multi-user Agent should distinguish authenticated identity, resource ownership, per-request authorization, targeted sharing, and browser-view isolation. It describes one Run as the authorization lifecycle for background research while emphasizing that the public Open ETF Agent v0.15.2 teaching build is local and single-user rather than a complete multi-user implementation.

    掘金 · 人工智能 · 🔥 0

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