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The author argues that many human approval gates for AI agents are ineffective rubber stamps, and proposes a framework for designing meaningful review mechanisms that actually catch errors.
Alfard is a local AI agent that requires user approval for irreversible actions, addressing security and trust concerns. It runs entirely on the user's machine and manages tasks like GitHub PRs and Notion tasks.
After 8 months of deploying AI agents on real operational tasks, the author shares five unexpected engineering challenges: per-capability permissions, credential isolation via a connector proxy, durable approval gates, hard budget caps, and out-of-process audit logs.