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ChatGPT agent: bridging research and action

openai.com

11–20 of 508 posts

Re: ChatGPT agent: bridging research and action

#13
post #3

I do not know what an agent is and at this point I am too afraid to ask.

Giving an LLM access to the command line so it can bash and curl and and python and puppeteer and rm -rf / and send an email to the FBI and whatever it thinks you want it to do.

Re: ChatGPT agent: bridging research and action

#14
It's very hard for me to imagine the current level of agents serving a useful purpose in my personal life. If I ask this to plan a date night with my wife this weekend, it needs to consult my calendar to pick the best night, pick a bar and restaurant we like (how would it know?), book a babysitter (can it learn who we use and text them on my behalf?), etc. This is a lot of stuff it has to get right, and it requires a lot of trust!

I'm excited that this capability is getting close, but I think the current level of performance mostly makes for a good demo and isn't quite something I'm ready to adopt into daily life. Also, OpenAI faces a huge uphill battle with all the integrations required to make stuff like this useful. Apple and Microsoft are in much better spots to make a truly useful agent, if they can figure out the tech.

Re: ChatGPT agent: bridging research and action

#15
It's smart that they're pivoting to using the user's computer directly - managing passwords, access control and not getting blocked was the biggest issue with their operator release. Especially as the web becomes more and more locked down.

> ChatGPT agent's output is comparable to or better than that of humans in roughly half the cases across a range of task completion times, while significantly outperforming o3 and o4-mini.

Hard to know how this will perform in real life, but this could very well be a feel the AGI moment for the broader population.

Re: ChatGPT agent: bridging research and action

#17
post #3

I do not know what an agent is and at this point I am too afraid to ask.

An workflow is a collection of steps defined by someone, where the steps can be performed by an LLM call. (i.e. propose a topic -> search -> summarise each link -> gather the summaries -> produce a report)

The "agency" in this example is on the coder that came up with the workflow. It's murky because we used to call these "agents" in the previous gen frameworks.

An agent is a collection of steps defined by the LLM itself, where the steps can be performed by LLM calls (i.e. research topic x for me -> first I need to search (this is the LLM deciding the steps) -> then I need to xxx -> here's the report)

The difference is that sometimes you'll get a report resulting from search, or sometimes the LLM can hallucinate the whole thing without a single "tool call". It's more open ended, but also more chaotic from a programming perspective.

The gist is that the "agency" is now with the LLM driving the "main thread". It decides (based on training data, etc) what tools to use, what steps to take in order to "solve" the prompt it receives.

Re: ChatGPT agent: bridging research and action

#18

It's smart that they're pivoting to using the user's computer directly - managing passwords, access control and not getting blocked was the biggest issue with their operator release. Especially as the web becomes more and more locked down. > ChatGPT agent's output is comparable to or better than that of humans in roughly half the cases across a range of task completion times, while significantly outperforming o3 and…

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