Judy shares her blood-and-tears experience managing AI Agent teams: traditional management skills like trust empowerment and incentive systems completely fail on AI. AI has no ego and doesn’t care about impact. Goal breakdown, closed-loop tracking, and quality gates are the keys. The Gate-6 verification mechanism evolved from multiple empty task failures.
ByteDance’s Volcano Engine launched OpenViking, redesigning AI Agent memory with a filesystem logic. The three-tier loading mechanism (L0/L1/L2) lets Agents check the directory before deciding whether to open files, reducing token consumption from 24.6M to 4.3M and boosting task completion rate from 35% to 52%.
AI Agents are evolving from chatbots into digital agents that can trade autonomously, but when AI can spend money on its own, verifying “who’s behind it” becomes crucial. World, Coinbase, Visa, and Mastercard are building identity verification infrastructure for the AI era, using zero-knowledge proofs and other technologies to let platforms verify that Agents represent real humans rather than malicious bots.
When an Agent says it’s done, that doesn’t mean it’s actually done — this is something we’ve learned the hard way at Judy AI Lab. Silent failures in scheduled tasks, a 40% rejection rate on deliveries forced us to design a five-stage self-review loop: from spec confirmation, implementation, code review, fix, to Xiaoyue’s QA scoring. After going live for over a month, the rejection rate dropped from 40% to 10%.