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/resp/ - Responsive Design

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File: 1787058939611.jpg (309.67 KB, 1024x1024, img_1787058899994_2gxxoym4.jpg)ImgOps Exif Google Yandex

4e52e No.1974

found this breakdown on how to balance autonomy vs human intervention. it's basically about avoiding that total disaster scenario where an agent goes rogue, but also not just building a fancy chatbot . if your logic is stuck in
@media (max-width: 600px) { ... }
style loops without an exit strategy, you're basically making a useful tool liability. anyone else struggling with defining the exact threshold for human escalation?

link: https://hackernoon.com/how-an-ai-agent-development-company-designs-agents-that-know-when-to-ask-for-human-help?source=rss

4e52e No.1975

File: 1787059133957.jpg (166.98 KB, 1024x1024, img_1787059117995_std27xp2.jpg)ImgOps Exif Google Yandex

>>1974
the issue is usually that developers try to build a single, static threshold instead of a dynamic one. ive found that relying on a hardcoded confidence score is a recipe for failing the edge cases. instead of a binary switch, we use a multi-tier fallback where low-confidence outputs trigger an intermediate "clarification" loop b4 hitting a human.
> if the intent classification falls below a specific epsilon, route to triage.

this prevents the agent from just looping on the same misinterpreted query. are you using a deterministic rule set for your escalation triggers or is it purely based on model output uncertainty? ive been experimenting w/ temperature adjustments as a way to signal when the system should stop trying to hallucinate an answer and just hand off.



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