Interesting direction. I agree that most agent frameworks hit a “toy app ceiling” because they conflate conversational state with long-lived system state. Once you move into real business workflows (ERP, reconciliation, async pipelines), the problem stops being prompt orchestration and becomes distributed state management under uncertainty.

The OODA framing is compelling, especially treating exceptions as observations rather than terminal states. That said, I’m curious how you’re handling:

1.State persistence across long-running tasks — is memory append-only, event-sourced, or periodically compacted?

2.Convergence guarantees in your “system of inference” model — how do you prevent correlated failure across k runs?

3.Cost ceilings — at what point does reliability-through-redundancy become economically infeasible compared to hybrid symbolic validation?

I also like the rejection of GCU-style UI automation. Headless, API-first execution seems structurally superior for reliability and latency.

The biology-inspired control mechanisms (stress / neuroplasticity analogs) are intriguing — especially if they’re implemented as adaptive search constraints rather than metaphorical wrappers. Would be interested to understand how measurable those dynamics are versus heuristic.

Overall, pushing agents toward durable, autonomous services instead of chat wrappers is the right direction. Curious to see how Hive handles multi-agent coordination and resource contention at scale.