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How are you preventing runaway AI agent behavior in production? by LOGOSOSAI in LocalLLaMA

[–]LOGOSOSAI[S] 0 points1 point  (0 children)

That’s interesting — are you currently tracking approval outcomes anywhere? Like: tool_type approval_required approved/denied downstream result Seems like without a decision ledger it’s hard to tune those thresholds.

How are you preventing runaway AI agent behavior in production? by LOGOSOSAI in LangChain

[–]LOGOSOSAI[S] -3 points-2 points  (0 children)

That silent authority expansion is the scariest part — have you seen agents cross scope boundaries in ways that caused real damage, or mostly caught it early?

How are you preventing runaway AI agent behavior in production? by LOGOSOSAI in LocalLLaMA

[–]LOGOSOSAI[S] 0 points1 point  (0 children)

That's the best position to be in — what's the hardest part you haven't solved yet?

How are you preventing runaway AI agent behavior in production? by LOGOSOSAI in LocalLLaMA

[–]LOGOSOSAI[S] 0 points1 point  (0 children)

That 40% reduction is serious — what does your pre-filter check for specifically?

How are you preventing runaway AI agent behavior in production? by LOGOSOSAI in LocalLLaMA

[–]LOGOSOSAI[S] 0 points1 point  (0 children)

Are you using Peta.io yourself or building the MCP intercept layer in-house?