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Ask HN: How are you preventing runaway LLM workflows in production?

1•HenryM12•1h ago
We’ve been pushing LLM backed workflows into production and are starting to run into reliability edges that observability alone doesn’t solve.

Things like:

- loops that don’t terminate cleanly

- retries cascading across tool calls

- cost creeping up inside a single workflow

- agents making technically “allowed” but undesirable calls

Monitoring here is fine. We can see what’s happening. The harder part is deciding where the enforcement boundary actually lives.

Right now, most of our shutdown paths still feel manual, things like feature flags, revoking keys, rate limiting upstream, etc.

Curious how others are handling these problems in practice:

- What’s your enforcement unit? Tool call, workflow, container, something else?

- Do you have automated kill conditions?

- Did you build this layer internally?

- Did you have to revisit it multiple times as complexity increased?

- Does it get worse as workflows span more tools or services?

Would appreciate any concrete experiences from teams running agents in production. Really just trying to figure out how to scale.

A Day in the Life of an Enshittificator [video]

https://www.youtube.com/watch?v=T4Upf_B9RLQ
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Guidelines for Writing Cryptography Specifications

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AI is creeping into election campaigns. NZ's rules aren't ready

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White House stalls release of approved US science budgets

https://www.nature.com/articles/d41586-026-00601-0
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Bounded Plasticity Simulation

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Clueless cops post seized crypto wallet password. $5M quickly stolen

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AI First Application Development

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How to Build Your Own Quantum Computer

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Vouch

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Nobody ever got fired for using a struct

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Origin of the Abbreviation i18n

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Fast Biology Bounties

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Vibe Theory: Mathematical Derivation of Aesthetic Vibe from Text

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2x Qwen 3.5 on M1 Mac: 9B builds a bot, 0.8B runs it

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Five People in Their 60s, 70s, and 80s Share How They Plan to Age at Home

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Microsoft Creative Writer (1993)

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