Nice work. The context efficiency numbers are legit! Going from 60K tokens to 80 is a massive difference.
Quick question: the cost attribution and audit traces are solid for tracking what happened, but what about when an agent does everything "right" and it still turns out to be the wrong call downstream? The grounding gate handles hallucinations, but if the agent picks a real tool, sends valid params, and the outcome is just... a bad decision, is there any way to unwind that? Or is that intentionally outside what the runtime is responsible for?
Also, the learning system is clever — promoting tools that work, demoting ones that don't. Have you thought about pointing that same feedback loop at the decisions themselves, not just which tool got picked? (guess that was 2 quick questions)
Quick question: the cost attribution and audit traces are solid for tracking what happened, but what about when an agent does everything "right" and it still turns out to be the wrong call downstream? The grounding gate handles hallucinations, but if the agent picks a real tool, sends valid params, and the outcome is just... a bad decision, is there any way to unwind that? Or is that intentionally outside what the runtime is responsible for?
Also, the learning system is clever — promoting tools that work, demoting ones that don't. Have you thought about pointing that same feedback loop at the decisions themselves, not just which tool got picked? (guess that was 2 quick questions)