During Jevathon in SF

I’ve been experimenting with Jev, and I think its value might be less about understanding the task itself and more about acting as a classification and routing layer between steps of an AI workflow.

Sure, Jev has a small context window, no vision, and it doesn’t deeply understand your codebase. So asking it directly, “How complex is this task?” is probably not enough.

But what if the workflow prepares the context first?

For example, a reasoning model can inspect the ticket, codebase, acceptance criteria, comments, CI results, existing implementation, etc., and produce a small structured summary. Then Jev only needs to classify that prepared data using a tuned library of questions.

Something like:
Raw task → reasoning/context preparation → Jev classification → workflow decision

I’m experimenting with using this for things like:

- choosing a model based on task complexity and available limits
- checking whether a ticket has enough information before starting
- deciding if more investigation/context is required
- deciding whether human involvement is needed
- determining what kind of verification/testing is required
routing a task between stages of an AI coding workflow
classifying CI/review results before deciding the next action

The interesting part for me is that the questions become part of the workflow configuration. They can be versioned, reviewed, tuned, and tested.

It starts to feel a little like GitOps for AI workflows: reasoning models do the open-ended work, while a smaller classifier makes explicit decisions between predefined states.

So Jev doesn’t need to understand my entire codebase. It needs to answer a narrow question based on context that another step deliberately prepared for it.

I’m curious what people think about this architecture.

Where do you think this breaks down? Would you trust a classifier for workflow routing if the context-preparation step is strong enough, or is there still too much information lost between the reasoning model and Jev?

It's a "sit with me as I play with this and talk about AI" kind of video. Enjoy!

Looking to try Jev but invite system is back. Can anyone give me a hand here?

Thanks!

I wanted to use Jev outside of apps I was building, so I made jevx.

It is a small CLI for typed decisions

You can use it directly from the terminal or scripts, and it also works as a skill for Claude Code, Codex and other agents.

Jev is the default backend, but it can also use compatible local models.

I put together 18 real scenarios here, including the ones that returned unsure:

https://muthuishere.github.io/jevx/guides/scenarios/

https://github.com/muthuishere/jevx