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On October 23, 2026, TinyFish released two technical guides: one defines a production-grade agentic search architecture, and the other summarizes lessons learned from building a deep research system. According to a MarkTechPost report, the architecture is built on four web primitives—TinySearch, TinyFetch, TinyBrowser, and TinyAgent—and runs a seven-stage control loop around a foundation model, covering task definition, retrieval planning, tool routing, evidence evaluation, and failure retry. In OpenBenchmarks tests with a fixed gpt-5.6-sol model and 100 tasks, the search-plus-fetch configuration achieved a 79.0% completion rate, with a median task cost of $0.050 and a runtime of 24 seconds; the search-only configuration achieved a 59.3% completion rate. Currently, this event has only the single source report mentioned above, and there have been no subsequent developments.
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TinyFish Breaks Down Production-Grade Agent Search Architecture and Deep Research ExperienceDevelopments
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Oct 23
MarkTechPost
TinyFish Breaks Down Production-Grade Agent Search Architecture and Deep Research Experience
TinyFish released two technical guides: one defines a production-grade agent search architecture, and the other summarizes lessons from building a deep research system. The architecture is built on four web primitives—TinySearch, TinyFetch, TinyBrowser, and TinyAgent—and runs a seven-stage control loop around a foundation model, covering task definition, retrieval planning, tool routing, evidence evaluation, and failure retry. In OpenBenchmarks tests with a fixed gpt-5.6-sol model and 100 tasks, TinyFish search plus fetch achieved a 79.0% completion rate, with a median task cost of $0.050 and a runtime of 24 seconds; under a search-only configuration, the completion rate was 59.3%.