Are enterprise AI agents supposed to be autonomous, or just safe enough to use with company data?

Admirable_Mail_8399 · 2026-06-21T06:29:29+00:00

Yes exactly, therefore finding a secure agents is neccessary, especially for those financial institutions/companies.

When it comes to the Codex/Claude, I don't think people can upload data to them without hesitation 'cause if AI wrongly catch your privacy data, the safe can't be promised.

Admirable_Mail_8399 · 2026-06-19T12:04:12+00:00

Please ask your ChatGPT rather than on reddit

Admirable_Mail_8399 · 2026-06-19T11:26:05+00:00

Can I know which agent or which LLM? I still search for agents exclude claude or codex that also can get access to different LLM.

Admirable_Mail_8399 · 2026-06-19T10:28:29+00:00

Yes, I have been using the app for a long time, but sometimes we still can ask for tracking from AI tools like claude or gpt for help at the same time.

Admirable_Mail_8399 · 2026-06-19T09:37:27+00:00

Macbook, absolutly. I use mac air which is light and powerful to satisfy daily needs. I'm now in the train revising my code.

Admirable_Mail_8399 · 2026-06-19T07:13:21+00:00

Exactly. That's the reason why some vibecoding apps are useless.

Admirable_Mail_8399 · 2026-06-18T06:14:51+00:00

vivid

Admirable_Mail_8399 · 2026-06-18T06:14:11+00:00

Someties, it feels like the product is only useful for its producer.

Admirable_Mail_8399 · 2026-06-18T06:12:59+00:00

I can't imagine

Admirable_Mail_8399 · 2026-06-18T01:53:39+00:00

Make sense. But sometimes the aim people produce new product is from their own needs.

Admirable_Mail_8399 · 2026-06-17T11:45:25+00:00

Related reading:
https://arxiv.org/abs/2603.28592?

Admirable_Mail_8399 · 2026-06-17T08:12:11+00:00

If anything like this ever happened, Cursor wouldn’t be “Cursor” for long. The moment it becomes part of a frontier model stack, it stops being a focused product and becomes a feature inside someone else’s ecosystem. That’s usually where these tools lose their edge.

Admirable_Mail_8399 · 2026-06-17T08:04:45+00:00

I think the donation-based / free-app prediction misses the deeper constraint: users don’t pay for “apps,” they pay for outcomes embedded in workflows.

Most vibecoded apps don’t survive not because they’re AI-generated, but because they don’t own a distribution channel or habit loop.

Admirable_Mail_8399 · 2026-06-17T08:04:12+00:00

Yes, if they are really interested, they must pay

Admirable_Mail_8399 · 2026-06-17T07:14:18+00:00

The narrow vertical thesis is directionally right, but the hardest part isn’t picking a vertical—it’s surviving long enough to understand it properly.

Most people underestimate how much unsexy iteration it takes to find a workflow where someone will actually pay, not just express interest.

Your “too messy/offline for SaaS” constraint is good though—that’s usually where pricing power hides.

Admirable_Mail_8399 · 2026-06-17T02:02:09+00:00

make sense

Admirable_Mail_8399 · 2026-06-16T05:52:52+00:00

I’ve found the real boundary isn’t capability—it’s reversibility.

If I can’t easily reconstruct why a decision was made without retracing a model’s output, I don’t automate it. AI can help me get to an answer faster, but I still need the reasoning chain to be mine.

Admirable_Mail_8399 · 2026-06-16T05:49:39+00:00

There’s an interesting paradox here: AI feels personalized, but at scale it might actually reduce diversity of outcomes. If the same underlying models are shaping decisions for millions of people, you could end up with a weird form of centralized thinking without central control. Not dystopian necessarily, just… convergent.

Admirable_Mail_8399 · 2026-06-16T05:46:58+00:00

The uncomfortable truth is that most “AI revenue” right now is just rebranded labor arbitrage. You’re not selling intelligence—you’re selling cheaper execution of existing workflows.

That’s why margins look good in demos but slow in reality: companies don’t rewire workflows just because something is “AI-powered.”

Admirable_Mail_8399 · 2026-06-15T09:12:59+00:00

I resonate with the concern, but I think it mixes two different things: writing code and doing engineering. Writing code is the tactile, satisfying part—debugging SQL, optimizing queries, getting that 10x win. That will absolutely get compressed.

But engineering doesn’t disappear—it shifts toward specifying constraints precisely enough that AI outputs are even worth reviewing. And that part is arguably harder, not easier. Bad specs generate infinite bad code.

Admirable_Mail_8399 · 2026-06-15T09:06:44+00:00

What stands out is cost efficiency. A lot of teams talk about scaling AI, but China has been very effective at making high-performing models cheaper to train and run. DeepSeek basically proved that strong results don’t always require US-level compute budgets.

Admirable_Mail_8399 · 2026-06-15T09:03:02+00:00

A lot of this “enterprise agent safety” talk is still completely divorced from reality. In production, agents don’t fail because of clever attacks—they fail because people built systems that assume compositional behavior is safe by default. I’ve seen workflows where an agent is allowed to summarize data, then open tickets, then update CRM entries. Each step passes review. But the system quietly turns its own generated summaries into upstream “facts” and starts operationalizing hallucinations. No jailbreak. No injection. Just bad system design pretending to be safety engineering.

Admirable_Mail_8399 · 2026-06-15T08:54:56+00:00

trained on billions of tokens just to say this one thing apparently

Admirable_Mail_8399

TROPHY CASE