I’ve been using this general pattern - a custom cli app for deterministic tasks, skills for the agent harness, run the skills in the agent and it produces artifacts for you by using the cli and its own agentic reasoning - a lot lately for work. Things like “give me an executive brief of the activity in these teams backlogs over the last month” and in 5-10 minutes I have a few page doc I can read that is cited with the tickets it analyzed and I don’t have to go bug people or ask them to do yet another task for me, just make sure your backlog is updated and detailed like normal practice. It’s awesome and really fits a useful spot between pure agent usage (which is hard to get consistent results from on repeat tasks) and not having to build/buy a full blown app for every random thing.
This approach works well, I agree. But I keep wishing that I could invert it. The architecture I feel like I keep yearning for, is a traditional CLI program that encodes most workflow knowledge/decisions as real code; but which does "just a little bit of coding agent invocation" during one specific workflow step.
Not sure how to accomplish this. Anyone have any suggestions? Are there libraries for this yet? (And how would they even work? It feels like, to do this right, there would have to be some background service that CLI software could expect to interact with via a well-known local IPC socket — similar to how e.g. the docker daemon works. But I'm unaware of any coding agent software/frameworks that expose such an IPC capability...)
Can you give some examples of the deterministic tasks? So in your example, was the deterministic task “fetch this team’s backlog”? And then the LLM parts are “process each backlog” and “combine a summary”?
I agree! I want to say I first saw this pattern in some work Simon Willison did (Rodney and Showboat). For certain workflows the pair of Skills + CLI give me a nice balance between the flexibility of LLMs and the consistency of a CLI.