I’ve been exploring Hive recently and what stands out is the move from prompt orchestration to persistent, stateful execution. For real ERP-style workflows, that shift makes sense.

Treating exceptions as observations instead of terminal failures is a strong architectural reframing. It turns brittleness into a feedback signal rather than a crash condition.

A few production questions come to mind:

1) In the k-of-n inference model, how do you prevent correlated failure? If runs share similar prompts and priors, independence may be weaker than expected.

2) How is memory managed over long-lived tasks? Is it append-only, periodically compacted, or pruned strategically? State entropy can grow quickly in ERP contexts.

3) How do you bound reflection loops to prevent runaway cost? Are there hard ceilings or confidence-based stopping criteria?

I strongly agree with the rejection of UI-bound GCU approaches. Headless, API-first automation feels structurally more reliable.

The real test, in my view, is whether stochastic autonomy can be wrapped in deterministic guardrails — especially under strict cost and latency constraints.

Curious to see how Hive evolves as these trade-offs become more formalized.