See my setup detailed in another sibling comment of yours, jj is just a small part of it, and you can probably get that with git too.
I’m already at full mental capacity planning and reviewing the work of two agents (one foreground which almost never asks for approval, and one background which never asks for approval).
I don’t really need the ability to juggle more of them, and noticing their messages is not a bottleneck for me, while I’m happy with the customizability and adaptability of my raw’er workflow.
Maybe if they’re as slow as codex…
Fair enough. I use git worktrees (with a script that creates the git branch, worktree and opens a new vs code workspace). You're right, managing more than about two active sessions at once is probably the limit though I'm somewhat hopeful that better tooling similar to the Agent Manager window here would allow me to scale a bit past that especially if some of those sessions are more design explorations.
Our SaaS has a built-in AI assistant that only performs actions for the user through our GraphQL API. We wrapped the API in simple MCP tools that give the model clean introspection and let us inject the user’s authenticated session cookie directly. The LLM never deals with login, tokens, or permissions. It can just act with the full rights of the logged-in user.
MCP still has value today, especially with models that can easily call tools but can’t stick to prompt. From what I’ve seen in Claude’s roadmap, the future may shift toward loading “skills” that describe exactly how to call a GraphQL API (in my case), then letting the model write the code itself. That sounds good on paper, but an LLM generating and running API code on the fly is less consistent and more error-prone than calling pre-built tools.