81–90 of 93 posts
Has anyone got an example of an agent doing work in production that is saving the company money and doing a genuinely worthwhile job (in other words it's not writing text that exists purely to fill space on a packet of chips)?
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The article on the multi-agent research is awesome. I do disagree with one statement in the building effective AI agents article - building your initial system without a framework sounds nice as an educational endeavor but the first benefit you get from a good framework is the easy ability to try out different (and cross-vendor) LLMs
Having built several systems serving massive user bases with LLMs. I think the ability to swap out APIs just isn't the bottleneck.. like ever. It is always the behavioral issues or capability differences between models. The frameworks just usually add more complexity, obscurity, and API misalignment. Now the equation can change IF you are getting a lot of observability, experimentation, etc. I think we are just reach…
It's a massive pain in the arse for testing though. Checking which out of X number of things performs the best for your use case is quite annoying if you have to have X implementations. Having one set that you swap out keys and some vars makes this massively easier.
Has anyone got an example of an agent doing work in production that is saving the company money and doing a genuinely worthwhile job (in other words it's not writing text that exists purely to fill space on a packet of chips)?
1. Agentic Automation: For every alert/ticket coming in, the agent does a pre-investigation across relevant APIs, DBs, etc, helping identify FPs and providing more context on real ones. Cuts down on human time and speeds up handling.
2. Vibes Investigation: The same agentic reasoning is used when spelunking, where beyond just text2sql, the LLM will spin 2-10 minutes to investigate Splunk, databricks, etc for you.
Underneath, the agent has tools like semantic layers over DBs, large logs/text/dataframe analysers, etc .
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Nothing works automagically. You still have to build in all the operational characteristics that you would for any traditional system. It's deceptively easy to look at some AI agent demos and think "oh, I can replace my team's huge mess of spaghetti code with a few clever AI prompts!" And it may even work for the first couple use cases. But all that code is there for a reason, and eventually it'll have to be reckoned…
Then wtf is the point of this?
Anything an AI agent does that is not that, can be done cheaply and deterministically by some code.
If code can replace humans, it can replace AI.
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That's more of a hobbyist thing I'd say. Corporations developing these things will of course want to use some centralized system that they trust. It's more efficient, they have more control over it, it's easier for average people to use, etc. A decentralized thing would be more for individuals who want more control and transparency. A decentralized public ledger would make it possible to verify that your agent, the a…
Good points. I agree that for most companies using centralised systems offers more advantages because of efficiency, control and user experience, but I wasn't arguing that decentralisation is better technically, just wondering if it might be necessary in the long run. If agents become more autonomous and start coordinating across platforms owned by different companies, it might make sense to have some kind of shared,…
But, that's just a guess. Maybe the combination of AI and automation adds something special to the mix where a global public ledger becomes more valuable (beyond the hobbyist community) and I'm just not seeing it.
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I believe the definition of workflows in this article is inaccurate. Workflows in modern engines do not take predefined code paths, and agents are effectively the same as workflows in these cases. The redefinition of workflows seems to be an attempt to differentiate, but for the most part an agent is nothing more than a workflow that is a loop that dynamically invokes things based on LLM responses. Modern workflow en…
You appear to be making the mistake of assuming that the only valid definition for the term "workflow" is the definition used by software such as https://airflow.apache.org/ https://www.merriam-webster.com/dictionary/workflow thinks the word dates back to 1921. There no reason Anthropic can't take that word and present their own alternative definition for it in the context of LLM tool usage, which is what they've don…
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I believe the definition of workflows in this article is inaccurate. Workflows in modern engines do not take predefined code paths, and agents are effectively the same as workflows in these cases. The redefinition of workflows seems to be an attempt to differentiate, but for the most part an agent is nothing more than a workflow that is a loop that dynamically invokes things based on LLM responses. Modern workflow en…
I think the distinction is more about the "level of railroading". Workflows have a lot more structure and rules about information and control flow. Agents, on the other hand, are often given a set of tools and a prompt. They are much more free-form. For example, a workflow might define a fuzzy rule like "if customer issue is refund, go to refund flow," while an agent gets customer service tools and figures out how to…
This defines how workflows are used with modern systems in my experience. Workflows are often not predictable, they often execute one of a set of tools based on a response from a previous invocation (e.g. an LLM call).
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I think the distinction is more about the "level of railroading". Workflows have a lot more structure and rules about information and control flow. Agents, on the other hand, are often given a set of tools and a prompt. They are much more free-form. For example, a workflow might define a fuzzy rule like "if customer issue is refund, go to refund flow," while an agent gets customer service tools and figures out how to…
Just to emphasize your point, below is a workflow I wrote for an LLM recently, to do language tagging (e.g., of vocab, grammar structures, etc). It's very different than what you'd think of as an "agent", where the LLM has tools and can take initiative. LLMs are amazingly powerful in some ways, but without this kind of "scaffolding", simply not reliable enough to make consistent choices. --- 1. Here are: a) a "langua…
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Nothing works automagically. You still have to build in all the operational characteristics that you would for any traditional system. It's deceptively easy to look at some AI agent demos and think "oh, I can replace my team's huge mess of spaghetti code with a few clever AI prompts!" And it may even work for the first couple use cases. But all that code is there for a reason, and eventually it'll have to be reckoned…
Then wtf is the point of this?