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It is worth pointing out that both of them are workflow tools first. The AI agent part is expressed though steps where a model is invoked. This opens the door to a number of issues including vulnerabilities.

My personal, and my I say biased, opinion is that these tools do not deliver on the agentic promise. AI agents require a completely different approach.



The definition of an agent is so vague now. What is the agentic promise and why wouldn't a workflow tool with LLM steps cannot deliver on this promise? Also what vulnerability are you referring to? I have no stakes in n8n or node red, but being able to embed LLMs in a workflow and conversing with a "workflow" seems to solve a lot of agentic/assistance problems (Make a report of revenue, and send an email report to these people,for example)

What is the completely different approach you are suggesting?


Beginner here, I sort for agree. I think these tools are good to make a few good demos and quickly sketch something but you hit the ceiling soon where every other thing you do needs more work. There a lot of tutorials online plus the promise of AI automation as the next "get rich quick" thing.

I am curious what is a good tool to build agents these days? I am checking out mastra, someone at the cafe told me about Inngest.. wondering what would you use?


Anthropic recommends not using frameworks and simply writing code against the LLM apis, at least to begin with.

https://www.anthropic.com/engineering/building-effective-age...


Check out DBOS. There are a bunch of sample apps here:

https://github.com/dbos-inc/dbos-demo-apps/tree/main/python

DBOS is an open source library that doesn’t require an external server.


If you want to build AI assistants for automating tasks, check out https://nelly.is.


There's a whole n8n subreddit where people are making customer call/customer agent flows.

Think of that what you will but it seems some of them make a profit off of it.

So, it must work somewhat?




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