Digital Craftsman and SWE

AI agents can produce scientific conjectures faster than institutions can test them. The useful system is therefore not the idea generator alone, but the evidence queue that decides what deserves contact with reality.

Google Quantum AI used error-correction signals to steer a processor while it ran. The deeper systems lesson is to separate immediate correction from bounded adaptation without letting either weaken the detector.

N17Q became more dependable when every long-running workflow declared what counted as progress, when uncertainty had reached its evidence limit, and how to preserve useful work without forcing completion.

A trace, replay, and policy system for testing long-running agent workflows without repeating their external consequences.

An evidence-backed document workflow where model output remains a reviewable proposal with provenance and revision history.

Hybrid document search and grounded answers designed to refuse fluently when the collection cannot support a claim.