Hi HN,I ve been working on an open-source CLI agent called Interbase:https://github.com/agentsorchestrationcompany/interbaseTwo 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 workflowAfter 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.