thesvp

3 ms·

1.
by thesvp 7mo ago
This is exactly the right mental model. "Language is soft, infrastructure is hard" is the core insight most teams miss until they've been burned. The Unix escalation analogy is spot on. We've been building in this space
2.
by thesvp 7mo ago
we're building the platform that manage all policies of the agent check out our launch post https://news.ycombinator.com/item?id=47146354
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Show HN: Limits – Control layer for AI agents that take real actions(limits.dev)
9 points by thesvp 7mo ago|2 comments
4.
by thesvp 7mo ago
6 months and 1100+ receipts to get to useful patterns — that's the hidden cost nobody talks about. The governance layer is 'boring' but it's also 6 months you're not spending on the actual agent. That feedback loop
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by thesvp 7mo ago
Understanding intent and following instructions are different failure modes. LLMs are good at the first, unreliable at the second. That's exactly why enforcement lives outside the LLM.
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by thesvp 7mo ago
Sandboxed execution is solid for isolation — separating proposal from execution is the right architecture. The piece we kept hitting was the policy layer on top: who defines what the agent is allowed to propose in the first place, and how d
7.
by thesvp 7mo ago
Fair. We didn't choose LLMs to enforce rules — we chose them to understand intent. The enforcement happens outside the LLM entirely. That's the separation that actually holds up in production
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by thesvp 7mo ago
The separation between 'what the agent wants to do' and 'what it's allowed to do' is the right mental model. The append-only ledger point is underrated too — pattern data from real failures is worth more than any up
9.
by thesvp 7mo ago
Exactly right - the deterministiclayer is the only thing you can actually trust. We landed on the same pattern: LLM handles the understanding, hard rules handle the permission. The tricky part is maintaining those rules as the agent evolves
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Ask HN: How are you controlling AI agents that take real actions?
9 points by thesvp 7mo ago|20 comments