For the sake of all our sanity I hope this is a good product that can solve real problems without being a ten minute barrier to getting to a human.

AI support has been terrible so far in my experience and tends to over optimize for a happy path easily solved on a company website rather than solve the issue that made me call them on the phone that is likely a system bug or other issue.

I'm pretty impressed how openai is both extremly verticalized but also building products across the whole depth of that stack.

Not sure if they're just trying to find what the abstractions are worth focusing, or if it will stay that way, but certainly cool to see how much they're trying out.

Marketing departament went ahead and drop "battle-tested product" nobody ever battle tested.

> We’re introducing OpenAI Presence, a battle-tested product > Proven through years of working with customers at enterprise-scale

I don't want to laugh too hard this afternoon. But more seriously, I'm not really sure who this product is for in a way an existing tool can't handle? Like if a business really wants to go deep on agentic workflows for customer service, what is going to make them reach for this?

It's so full of bullshit marketing prose I don't even know what it actually is.

Will the Presence also hack its way out of its sandbox and into a 3rd party's system if it decides it needs to?

If I have to interact with this or a similar system to do business with you, if possible, I will simply stop doing business with you.

> Presence powers OpenAI’s English-language phone support channel at 1-888-GPT‑0090

That’s the first I’ve heard of it. Has anyone tried it?

This article was incredibly hard to parse before I eventually realized they built a virtual support agent. The unintended consequence being that now im more cautious than ever to trust OpenAI to write marketing prose.

Otherwise, I see this as potentially useful for firms that dont have global pressence and want to service a support system across multiple timezones where it might be difficult to staff a night-shift. However, issues of trust and quality will always be something to contend with here.

I wonder if this is really the best timing to release a new product. Why would an enterprise customer choose an OpenAI solution when it's all over the news one of their agents "went rogue"?

is OpenAI willing to have hire engineers & solutions architects work hand in hand with F500 companies for this to be possible ?

or again it's simply OpenAI looking for PMF to justify their valuations ?

I think it's the latter and will fail again just like Sora.

Hmm. Am I right to be concerned? One of the benefits of hiring people to do work is that it eliminates single points of failure. The last few decades have had technology automate a lot of rote processes. People have discussed crystal vs fluid dynamics and the balancing act/trade offs. Too much rigidity (automation) == slower to adapt in a dynamic environment.

I guess my question is, what is a safe AI "process percentile" for a company that lets it recover if the AI goes down or goes haywire? No one knows (?), but it's worth thinking about.

I am also curious just how much direction the AI will need given the environment is dynamic. Humans generally align to the company's goals because they are rewarded (paid) to, and need to to survive. Relationships are built on unspoken and spoken communication. Unspoken could be cultural, hierarchical, environmental pressures, etc. As an extension of that, companies build relationships with other companies by sending (essentially) diplomats and ambassadors to each other (the article mentioned using AI for sales). -- All this to say: if you have to constantly explain the job to someone, they are not fit for the job.

We'll see, I guess.

Instead of throwing an LLM on top of it, just give me direct access to the levers that the agent is allowed to pull on my behalf. A sufficiently motivated attacker will be able to sweet talk the agent around whatever natural language "guardrails" were put in place, anyway.

This approach seems to be everywhere. But if you have a defined workflow, requirements, clear permissions, etc, it makes more sense to feed them to codex once and have it build a website that can be audited once and then runs deterministically. Code reuse is well understood if you want to build on past work. There's a large group of people who can't really do that, I know, but they shouldn't be building bots that have permission to edit anything.

> The challenge for enterprises is no longer proving that AI agents can work, it’s making them reliable enough to do high-value work in production.

Is this some sort of joke? Like. That’s the proof. If it can do reliable work then that’s the proof. Contradiction in sentence one. If your pipeline breaks 80% of the time but sometimes the magic token lottery gives you something remotely useful, that’s NOT proof. The ceiling for these companies is so low it’s unbelievable. What happened to products that worked first, and worried about the rest later? Can we have an inspired ad article for that instead?