bhaviav100

5 ms·

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by bhaviav100 3mo ago
I have been in revenue operations for a decade. For the last two weeks, I've started a personal 100-day challenge to study and build around what I've been calling a "Company Brain." I'm trying to understand what a c
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Show HN: I visualized how AI agent systems accidentally become org charts(jointherevolution.today)
3 points by bhaviav100 5mo ago|0 comments
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by bhaviav100 6mo ago
This is great visibility..just checked the website..I will try this over weekend
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by bhaviav100 6mo ago
Especially when you are running multiple agents for research
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by bhaviav100 6mo ago
Sounds exciting..I liked the token counter concept. Didn't thought about it though. Do you have a GitHub repo?
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by bhaviav100 6mo ago
I agree. This is huge market opportunity. I don't know whether anyone is building this
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by bhaviav100 6mo ago
I haven't tried this .will do
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by bhaviav100 6mo ago
If only there was a way to manage contexts better
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by bhaviav100 6mo ago
Can you try this and let me know whether this helps you https://authority.bhaviavelayudhan.com/
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by bhaviav100 6mo ago
The only way to make something better is to use it more
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by bhaviav100 6mo ago
Exactly why I built this. But cost control is not an entirely policy problem. Policies are just guidelines.
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by bhaviav100 6mo ago
This is interesting and I would love to understand more on this..is there a GitHub which I can look at? Here's something which would help you with another perspective on the contexts https://authority.bhaviavelayudhan.com&#
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by bhaviav100 6mo ago
Both yes and no .we don't have a way to predict or forecast this
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by bhaviav100 6mo ago
Companies won't survive with seats pricing https://www.theoperatorscircle.com/journal/36
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by bhaviav100 6mo ago
hmm interesting model routing + specialized MDs makes sense for cost efficiency. I’m seeing a different failure mode though that even with good routing, agents are looping or retrying and burning my money.
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by bhaviav100 6mo ago
yes, compaction and smaller models help on cost per step. But my issue wasn’t just inefficiency, it was agents retrying when they shouldn’t. I needed visibility + limits per agent/task, and the ability to cut it off, not just optimize
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Ask HN: How are you keeping AI coding agents from burning money?
8 points by bhaviav100 6mo ago|32 comments
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by bhaviav100 7mo ago
where is the link to Sarah?
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by bhaviav100 7mo ago
DMed you. I would love to check this out
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by bhaviav100 7mo ago
Sent you a DM
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by bhaviav100 7mo ago
Interesting to see verification moving into the agent payment layer. are you seeing more risk from “incorrect execution” vs “unauthorized execution”. Feels like both problems are emerging at the same time as agents start touching money.
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by bhaviav100 7mo ago
The separation between “agent reasoning” and “execution authority” is definitely becoming a real design pattern. what kinds of actions people are most afraid to let agents execute today. Is it mostly infra mutations, or business-facing act
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by bhaviav100 8mo ago
This matches what I’ve been seeing too. I’ve been building a similar enforcement layer, but focused first on customer-facing AI systems where mistakes create contractual or financial obligations rather than just infra damage. One thing that
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by bhaviav100 8mo ago
I’ve been experimenting with exactly this pattern. I built a small authority gateway that sits between agents and downstream systems and forces all high-risk actions through deterministic policy before execution. In a v2 iteration I just sh
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by bhaviav100 8mo ago
I’ve been working on a small experimental gateway that sits between agents and customer-facing execution paths and forces decisions through policy + approval before anything irreversible happens. v2 I just shipped adds: • risk scoring on dr
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Show HN: An authority gate for AI-generated customer communications(authority.bhaviavelayudhan.com)
4 points by bhaviav100 9mo ago|0 comments
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by bhaviav100 9mo ago
This isn’t about relative intelligence. Humans can be held accountable after the fact. Systems can’t. Once execution is automated, controls have to move from training and review to explicit enforcement points. Intelligence doesn’t change th
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by bhaviav100 9mo ago
These are two different control problems. Training governs what a model tends to say. Authority governs what is allowed to be acted on. You can’t pre-block bad advice, but you can pre-block unapproved financial or contractual actions. That’
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by bhaviav100 9mo ago
I don’t call it a failure of the AI. I agree it’s doing exactly what it was trained to do. The failure is architectural: once AI is allowed to draft at scale, “don’t feed it commitments” stops being a reliable control. Those patterns exist
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by bhaviav100 9mo ago
That’s true today, and it works as long as humans are the primary actors. The break happens when AI drafts at scale. Training + sampling are after-the-fact controls. By the time a bad commitment is found, the customer expectation already ex

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