I’ve been running into a recurring issue while working with multiple local AI tools and workflows — everything becomes fragmented very quickly. Even in a local setup, you end up with: – Separate interfaces – No shared context between tools – Manual handoffs between steps This gets worse as soon as you try to chain tasks together. I started experimenting with a local orchestration layer to unify this. The goal isn’t automation for its own sake, but coordination: – Passing context between tools without tightly coupling them – Keeping execution predictable (not a black box) – Avoiding “yet another dashboard” What’s been interesting so far: – Task routing is relatively easy – Context management is the hard problem – Tight coupling solves short-term issues but breaks flexibility long-term – Fully autonomous execution quickly becomes opaque and hard to trust Right now I’m leaning toward: – Isolated tools – A thin coordination layer – Approval-gated execution instead of full autonomy Limitations I’m still working through: – How to persist meaningful context without over-engineering it – Preventing the orchestration layer from becoming its own source of complexity – Balancing flexibility vs predictability Curious how others here are approaching: – Multi-step AI workflows – Context sharing between tools – Orchestration vs direct tool usage Feels like this is where things start breaking down as systems scale beyond a couple of tools. submitted by /u/New-Time-8269
Originally posted by u/New-Time-8269 on r/ArtificialInteligence