"Over the last few months, we've noticed a significant shift in how people build. As AI models become more capable at reasoning, we've noticed that developers are increasingly relying on new coding agents such as Claude Code/OpenClaw to handle complex tasks." is true when your primary users are developers. But no-code workflow is still valuable at scenarios where determinism is required.
I'm building an agentic compliance platform where every procedure needs to follow company policies and operated on the rigid plan, but the procedure itself is different for every customer. Coding agents could in principle generate the necessary workflow for each company, but they need to integrate with millions of other applications in the company, easy to review and modify.
We built our own workflow builder because every other existing solution is too complicated and doesn't meet compliance requirements. The workflow builder itself was coded by Claude, and it's working wonderfully.
So flowise's shutdown is because their business is targeting a specific sector which doesn't need it anymore, not because workflow builder itself is not useful.
Congratulations on your launch! 4 years of work is certainly remarkable perseverance.
The sync engine feature looks very interesting to me. There have been quite a few products available on the market today, but none has achieved a dominant share yet. So if this is your main strength, I'd like to see more demos built local first.
Curious if you considered shipping the engine itself as a standalone infra piece.
I'm a vercel customer, and I like using vercel AI SDK and Chat SDK. But I found myself moving away from vercel and next.js whenever I start a new project. I wish they maintain the technical standards while achiving commercial success.
If you want it to deeply research something pro is great. I had a problem I just couldn’t find with my oven so I gave it a lot of information and it went off on its own for about 2 hours and then gave me what I needed to fix the problem (fan was turning off too quickly which was causing the panel to overheat). I have no idea how it figured it out and I couldn’t find anything after hours of googling so it was very impressive. I even went and googled for it once I knew what the problem was and I still couldn’t find the solution that it came up with.
Thanks for sharing this experience. Does it cost a lot of token in the deep analysis - which will make the $100 plan much quicker to drain all budgets.
I think it’s going to be very hard to blow through your tokens just using chat. I mostly bought the plan so I could use Codex and on the $200 a month plan I’ve basically been using it 15 hours a day almost nonstop and I don’t run out of tokens for the week.
Congratulations! The difference between pure agentic exploration and deterministic steps is spot on. Runbooks give ops more confidence on the data exploration and save time/context.
Curious how much savings do you observe from using runbook versus purely let Claude do the planning at first. Also how the runbooks can self heal if results from some steps in the middle are not expected.
>> how the runbooks can self heal if results from some steps in the middle are not expected.
Yeah this is a very interesting angle. Our primary mechanism here is via agent created auto-memories today. The agent keeps track of the most useful steps, and more importantly, dead end steps as it executes runbooks. We think this offers a great bridge to suggest runbook updates and keep them current.
>> Curious how much savings do you observe from using runbook versus purely let Claude do the planning at first.
Really depends on runbook quality, so I don't have a straightforward answer. Of course, it's faster and cheaper if you have well defined steps in your runbooks. As an example, `check logs for service frontend, faceted by host_name`, vs. `check logs`. Agent does more exploration in the latter case.
Re: savings - it depends on the use case. For example, one of our users set up a small runbook to run a group-by-IP query for high-throughput alerts, since that was their most common first response to those alerts. That alone cuts out a couple of minutes of exploration per incident and removes the variability of the agent deciding what data to investigate and how to slice it.
In our experience, runbooks provide a consistent, fast, and reliable way of investigating incidents (or ruling out common causes). In their absence, the AI does its usual open-ended exploration.
reply