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by jmanhype 100 days ago
Good points across the board.

Threshold control — yes, that's the plan. Right now it's a single 0.8 cutoff which is obviously too blunt. A social media agent vs a client-facing email agent have completely different risk profiles. Building per-channel thresholds into the next release.

Cold start seeding — we actually do something close to this already. The rules system lets you pre-load corrections before the agent handles its first real conversation. But you're right that 5-10 reference outputs would be even better than corrections. That's a cleaner onboarding UX. Adding it to the backlog.

Corrections as structured context vs fine-tuning — glad someone else sees this the same way. The portability argument is the one that convinced me. If you can export your corrections as JSON and bring them to another provider, that kills the lock-in problem. We store them as structured records in Supabase right now.

Correction categories — this is smart and we're halfway there. The dashboard already groups corrections by trace/conversation, but not by pattern type (tone, audience, missing context). That's a better abstraction. Would make it much easier to spot systemic issues instead of whack-a-mole-ing individual responses.

Appreciate the detailed feedback. Curious — are you running agents in production yourself?

1 comments

Thanks! Threshold control is on the roadmap. Right now the confidence gating works at two levels: auto-approve (high confidence, like correcting a greeting style) and suggest-only (lower confidence, needs manual confirmation from dashboard). The correction-to-rule pipeline is the key mechanic — once you confirm a correction, it becomes a permanent behavioral rule injected at every boot. The agent literally cannot make that mistake again. Since launch we have added a 7-agent autonomous marketing crew running on the same memory infrastructure as proof it works in production.