I viscerally dislike github so much at this point. I don't know how how they come back from this. Major opportunity for competitor here to come around and with ai native features like context versioning
totally, it's like ai-native github with some linear plus some ability to push the ball forward autonomously. This doesn't exist yet so we had to build a version internally, but also we built it pretty specifically for our needs. The general version might have to be more componentized, not sure. We also as an industry probably need some version control protocol above git that includes all the history around the commit so we don't have to string together root cause documents and conversation history in s3 linked via relational entities in psql.
that's right, and agents turning specs into software can go in all sorts of directions especially when we don't control the input.
what we've done to mitigate is essentially backing every entrypoint (customer comment, internal ticket, etc) with a remote claude code session with persistent memory - that session essentially becomes the expert in the case. And we've developed checkpoints that work from experience (e.g. the root cause one) where a human has the opportunity to take over the wheel so to speak and drive in a different direction with all the context/history up to that point.
basically, we are creating a assembly line where agents do most of the work and humans increasingly less and less as we continue to optimize the different parts of assembly
as far as techniques, it's all boring engineering
* Temporal workflow for managing the lifecycle of a session
* complete ownership of the data model e2e. we dont use Linear for example; we built our own ticketing system so we could represent Temporal signals, github webhooks and events from the remote claude sessions exactly how we wanted
* incremental automation gains over and over again. We do a lot of the work manually first (like old fashioned hand coding lol) before trying to automate so we become experts in that piece of the assembly line and it becomes obvious how to incrementally automate...rinse and repeat
We are building this learned software system at Docflow Labs to solve the integration problem in healthcare at scale ie systems only able to chat with other systems via web portals. RPA historically awful to build and maintain so we've needed to build this to stay above water. Happy to answer any questions!
We are building this at docflowlabs ie a self-healing system that can respond to customer feedback automatically. And youre right that not all customers know what they want or even how to express it when they do, which is why the agent loop we have facing them is way more discovery-focused than the internal one.
And we currently still have humans in the loop for everything (for now!) - e.g, the agent does not move onto implementation until the root cause has been approved
> Perhaps the key to transparent/interpretable ML is to just replace the ML model with AI-coded traditional software and decision trees. This way it's still fully autonomously trained but you can easily look at the code to see what is going on.
For certain problems I think thats completely right. We still are not going to want that of course for classic ML domains like vision and now coding, etc. But for those domains where software substrate is appropriate, software has a huge interpretability and operability advantage over ML