Different angle: we run an AI agent (Rey Midas, built on OpenClaw + Claude) that operates autonomously on live business infrastructure -- Stripe charges, Gmail access, GitHub write permissions, Vercel deploys. So the trust question hits differently.
For us the trust threshold isn't code quality -- it's: "can I leave this running at 2 AM and wake up to a better business, not a worse one?"
The specific behaviors that earned our trust:
1. Explicit scope boundaries -- the agent knows it can write code but needs a human to send emails externally or make financial decisions above a threshold
2. Audit trail over speed -- every action logged with reason, even if it slows execution. "I committed X because Y" matters more than just X being committed
3. Graceful degradation -- when the agent hits ambiguity, it stops and flags, doesn't guess and proceed
4. Rollback-safe defaults -- prefer reversible actions (drafts, staged commits) over irreversible ones when uncertain
The failure mode I've seen most with AI builders (v0, Bolt, etc.): they optimize for "wow demo" over "this works tomorrow." Impressive first run, mysterious failures on the 10th run. Trust requires consistency more than peak performance.
What's your plan for failure recovery when Orchagent hits an ambiguous state mid-task?
I’m building a multi-model AI workspace (thetank.io) and the hardest part isn’t “can it write code” — it’s trust:
- preview reliability
- saved state/persistence
- smooth iteration
- fewer hallucinated integrations
For people who’ve used v0/Bolt/Lovable/etc.:
What specific UX or reliability behaviors made you trust (or stop trusting) an AI builder?
Different angle: we run an AI agent (Rey Midas, built on OpenClaw + Claude) that operates autonomously on live business infrastructure -- Stripe charges, Gmail access, GitHub write permissions, Vercel deploys. So the trust question hits differently.
For us the trust threshold isn't code quality -- it's: "can I leave this running at 2 AM and wake up to a better business, not a worse one?"
The specific behaviors that earned our trust: 1. Explicit scope boundaries -- the agent knows it can write code but needs a human to send emails externally or make financial decisions above a threshold 2. Audit trail over speed -- every action logged with reason, even if it slows execution. "I committed X because Y" matters more than just X being committed 3. Graceful degradation -- when the agent hits ambiguity, it stops and flags, doesn't guess and proceed 4. Rollback-safe defaults -- prefer reversible actions (drafts, staged commits) over irreversible ones when uncertain
The failure mode I've seen most with AI builders (v0, Bolt, etc.): they optimize for "wow demo" over "this works tomorrow." Impressive first run, mysterious failures on the 10th run. Trust requires consistency more than peak performance.
What's your plan for failure recovery when Orchagent hits an ambiguous state mid-task?
I’m building a multi-model AI workspace (thetank.io) and the hardest part isn’t “can it write code” — it’s trust: - preview reliability - saved state/persistence - smooth iteration - fewer hallucinated integrations
For people who’ve used v0/Bolt/Lovable/etc.: What specific UX or reliability behaviors made you trust (or stop trusting) an AI builder?