[foundatron]

Appreciate the thoughtful comment. I think there's a key distinction though: this isn't a conversational agent pipeline where you need to trace reasoning chains.

The attractor loop is closer to gradient descent than to an agent conversation. Generated code is treated as opaque weights, and only externally observable behavior matters (scored 0-100 by an independent LLM judge against holdout scenarios). "Things going sideways" just means the satisfaction score is low on that iteration, which naturally feeds back as context for the next one. Build failures, test failures, partial correctness: they're all just points on a convergence curve rather than catastrophic failures requiring forensic replay.

So the observability you need shifts from "what did the agent think at step 12?" to "is the loss curve trending down?" We persist per-iteration satisfaction scores, failures, and token costs, which gives you the audit trail. But it's a pretty compact one: a number, a list of failing scenarios, and a cost.

The spec durability point is a good one to raise. In this case specs aren't documentation that drifts from code over time. They're the actual input to the system. If the spec is wrong, you fix the spec. The generated code is disposable by design.

You're absolutely right that multi-run observability becomes important as this scales though. Watching N specs converge simultaneously will need a proper dashboard. But it's N loss curves rather than N conversation traces, which should be fundamentally simpler to reason about.