I think this is the challenge and the dissonance. For something to truly run autonomously you need to provide it some many constraints that it almost loses its usefulness. I've tried using AI, or at least looked into what I could use AI for to automate marketing tasks and I just don't think I can seriously set up a workflow in n8n or AgentKit that would produce sufficiently good results without me jumping in. That said, AI is incredibly helpful in this semi-autonomous mode with the right parameters, to the point of the parent comment.
Observability is a good guess, but I'd venture to guess that the conversations going on internally are about how to capture value across the entire stack. I wouldn't be surprised if we hear about them acquiring either a database/warehouse company and/or an analytics solution. Or vice versa, them getting acquired by a bigger player that wants to offer more connectors and data modeling functionality.
This is a really great comparison to draw. This actually made me think that this feeling of going from mastering a craft to working on large scale systems is probably how someone who was passionate about cars felt when they went from building cars one by one, knowing how the whole machine works to then having to take a job on an assembly line.
Fortunately I think anything pertaining to vibe coding/engineering/analytics is still more enjoyable and less grim than working on an assembly line, but the human feelings remain nonetheless.
I think the sentiment in this post is shared with most folks: We don't need yet another workflow tool. That said, I don't think no and low (or even all) code are mutually exclusive. Today these tools definitely speak to an audience that's technical enough to know how to get around in these tools but not so technical that they know how to deploy, monitor and maintain these workflows. What will happen with time is that any complexity that does exist in these low-code tools will be abstracted away with AI but you can dig into deeper layer if you are technical. This might be that very last point you're making?
> How can we make code generation models better at writing LLM powered workflows/agents
For context, the approach we've taken at Fabi is that you can manually build the workflows or you can ask the AI to entirely build it out for you and you can pick up where the AI left off at any point and edit the code yourself -- we're not a generic workflow solution like n8n or AgentKit, we're 100% focused on data analysis, but the same should be easily applicable to these solutions.
> The best way to avoid the red ocean is to build for an obscure and complex niche.
This seems like the counter-argument... You need to build something incredibly different. You need to message differently, you need to distribute differently.
If the argument is that convincing the world that you're different is harder than ever I buy that. So much fluff and noise out there that it's harder than ever to break through that noise and cut through the skepticism. But for that, it's more important than ever to be different.
I'm curious to see how this plays out when it comes to deploying and maintaining production-grade apps. I know relatively little about infrastructure and DevOps, but that's the stuff that actually always seems complicated when it goes from going to MVP to production. This question feels particularly important if we're expecting PMs and designers to be primary users.
That said, I'm super excited about this space and love seeing smart folks putting energy into this. Even if it's still a bit aspirational, I think the idea of cutting down time spent debugging and refactoring and putting more power in the hands of less technical folks is awesome.