I've been working on an open-source CLI agent called Interbase:

https://github.com/agentsorchestrationcompany/interbase

Two ideas motivated a lot of the project.

The first is that long-running agent workflows shouldn't be restricted to a small number of frontier models.

Many recent agent products are beginning to support persistent tasks, background work, and goal-oriented workflows. I think those capabilities are useful abstractions independent of the underlying model.

Interbase includes a `/goal` command that allows work to be organized around long-running objectives and supports more than 135 providers and 4,800+ models. The goal is to let users choose the model that works best for them rather than forcing a specific provider because a particular workflow feature only exists there.

The second idea is that AI workflows should be reusable in the same way shell workflows are.

Interbase includes `/aliases`, which allows users to create shortcuts for workflows they run frequently. For example, a user might create aliases such as:

`gcm` → preferred git commit workflow

`review` → code review workflow

`ship` → release readiness workflow

After a while these become muscle memory in much the same way traditional shell aliases do.

The project also includes encrypted remote access, and one of the next areas I'm exploring is computer use capabilities that can work across a broad range of models rather than a handful of specialized offerings.

I'm curious whether others think long-running goals and reusable workflows should live above the model layer, or whether they belong as model-specific capabilities.

Happy to answer questions about the implementation or design decisions.