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Ask HN: Examples of agentic LLM systems in production?

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Re: Ask HN: Examples of agentic LLM systems in production?

#11

Post by bluejay2387 »

As a side note, while I know of several language model based systems that have been deployed in companies, some companies don't want to talk about it:

1. Its still perceived as an issue of competitive advantage

2. There is a serious concern about backlash. The public's response to finding out that companies have used AI has often not been good (or even reasonable) -- particularly if there was worker replacement related to it.

It's a bit more complicated with "agents" as there are 4 or 5 competing definitions for what that actually means. No one is really sure what an 'agentic' system is right now.

Re: Ask HN: Examples of agentic LLM systems in production?

#12

Post by austinkhale »

bluejay2387 wrote: post #11

As a side note, while I know of several language model based systems that have been deployed in companies, some companies don't want to talk about it: 1. Its still perceived as an issue of competitive advantage 2. There is a serious concern about backlash. The public's response to finding out that companies have used AI has often not been good (or even reasonable) -- particularly if there was worker replacement relat…

This has been my experience. Lots of companies are implementing LLMs but are not advertising it. There's virtually no upside to being public about it.

Re: Ask HN: Examples of agentic LLM systems in production?

#13

Post by SebaSeba »

shmatt wrote: post #9

You'd probably have to define agents first. What large / mega caps call agents is LLM + RAG + API Calls to read data and trigger jobs. And there are plenty of those online

Yes, that's what it seems to me also, that often a RAG or similar is branded as an "agent". Though I personally understand an LLM agent as something that takes input x to use in LLM inference and then uses the output from that inference to create a new input for another LLM inference that includes the first output and so on, and repeats this >1 times.

Re: Ask HN: Examples of agentic LLM systems in production?

#14

Post by bwfan123 »

DataDaemon wrote: post #6

Agentic workflow is great only for demos without real business cases. Each agent can hallucinate, which will pass this hallucination to another agent. In the end, you have just garbage. But... It's better to be silent, we still need to inflate this bubble.

there are workflows where the outputs are "narratives" - an example is customer support. another example is summarization of text. characteristic of these use cases is that there is no one right answer. in these use cases agents fit in well.

issue however, is that the agents cannot be chained. ie, chaining requires deterministic outputs and not narratives.

Re: Ask HN: Examples of agentic LLM systems in production?

#15

Post by remoquete »

austinkhale wrote: post #5

I know of many, many LLM systems in production system, since that's what I've been helping companies build since the start of the year. Mostly it's pretty rote automation work but the cost savings are incredible. Agentic workflows are a much higher bar that are just barely starting to work. I can't speak to their efficacy but here's a few of the ones that are sort of starter-level agents that I've started seeing some…

Cost saving as in...? Hopefully not saving through making human employees redundant.

Re: Ask HN: Examples of agentic LLM systems in production?

#16

Post by cloudking »

IMO "Agents" are a marketing term, they are simply software that use LLMs somewhere in the backend. Often daisy chained into a series of operations that may involve additional LLM calls or calls to other internal/external services.

One we've been using for meeting notes + action items works quite well https://fireflies.ai

Re: Ask HN: Examples of agentic LLM systems in production?

#17

Post by frgtpsswrdlame »

bwfan123 wrote: post #14
DataDaemon wrote: post #6

Agentic workflow is great only for demos without real business cases. Each agent can hallucinate, which will pass this hallucination to another agent. In the end, you have just garbage. But... It's better to be silent, we still need to inflate this bubble.

there are workflows where the outputs are "narratives" - an example is customer support. another example is summarization of text. characteristic of these use cases is that there is no one right answer. in these use cases agents fit in well. issue however, is that the agents cannot be chained. ie, chaining requires deterministic outputs and not narratives.

>characteristic of these use cases is that there is no one right answer

I think what you mean is that they work best in cases where it's very hard to measure how well they are working.

Re: Ask HN: Examples of agentic LLM systems in production?

#19

Post by WinH »

We use LLM agents to do proofreading and editing of transcripts after they are edited by people. They are good at applying our customer's specific requirements (e.g. capitalization, formatting, etc.) without us having our folks worry about any of that. We use https://transcriberai.com or https://otter.ai/ (there are a bunch) to create the first transcript for our transcriptionists.

Re: Ask HN: Examples of agentic LLM systems in production?

#20

Post by lewisleclerc »

SebaSeba wrote: post #13
shmatt wrote: post #9

You'd probably have to define agents first. What large / mega caps call agents is LLM + RAG + API Calls to read data and trigger jobs. And there are plenty of those online

Yes, that's what it seems to me also, that often a RAG or similar is branded as an "agent". Though I personally understand an LLM agent as something that takes input x to use in LLM inference and then uses the output from that inference to create a new input for another LLM inference that includes the first output and so on, and repeats this >1 times.

Yeah. An agentic workflow is nothing but implementation of execution of a bunch of tasks and each task takes a little bit of help from the LLM. Honestly I believe this is applicable to companies that have workflows having a lot of manual tasks and automation of these workflows could be easier with the help of LLM agents.
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