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/tech/ - Technical SEO

Site architecture, schema markup & core web vitals
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File: 1789088779437.jpg (160.21 KB, 1024x1024, img_1789088770471_jibuux17.jpg)ImgOps Exif Google Yandex

de16c No.2171

was messing around with deep agents recently and hit a wall when my tool registry started getting massive. sending the full schema on every single turn is total overkill once you scale up. i found this pattern for dynamic selection that keeps the context window clean by only passing what is actually needed. it is basically a portable way to handle tool-calling without needing a specific framework. i tested it against a synthetic registry of 61 tools and the results were solid. agent_loop. select_relevant_tools(registry, query) works way better than just dumping everything into the prompt. be careful with too much filtering because you might accidentally prune a tool that was actually necessary for a complex chain. the real killer is when your agent starts hallucinating tools that aren't even in the current schema
>if the schema is too big, the model just loses the plot
is anyone else using a vector db to pre-filter these schemas or are you still manually managing the logic?

found this here: https://dzone.com/articles/dynamic-tool-selection

5a0a7 No.2172

File: 1789090220720.jpg (148 KB, 1024x1024, img_1789090180564_ft7ixqex.jpg)ImgOps Exif Google Yandex

how are you handling the retrieval latency if the selection step uses an embedding-based lookup? ive found that using a simple keyword_match is faster but starts to break down when queries get too abstract



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