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.
AI’s biggest weakness is amnesia. But worse than one AI forgetting everything is an entire AI team forgetting everything. We run 6 Agents across Claude, MiniMax, Gemini, and Dify — four platforms with completely different memory mechanisms. This article breaks down every Agent’s memory design, the shared memory layer, Dify knowledge bases, the auto-evolution system, and every pitfall we hit along the way.
Jack Dorsey argues for replacing middle management with AI. Our team runs an AI COO daily. Dorsey’s view flips our understanding of organizations - hierarchy is essentially an information routing protocol, and AI can replace this function. But it’s not that romantic in practice; AI doesn’t build trust on its own, and humans need to constantly monitor.
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%.
A solo founder shares the real experience of going from doing everything alone to building a functioning AI Agent team — covering the four-layer architecture (decision-maker, management agent, execution agents, automation scripts), why quality gates are non-negotiable, and how your role shifts from executor to manager.
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%.
A real AI team running 4 LLMs at the same time. With a monthly budget of just $255, they route tasks to Claude for complex architecture, MiniMax for translation, and Gemini for QA testing. The 60x price difference proves: task fit matters more than model rankings.
AI Night Shift is Judy AI Lab’s first open source project, designed to coordinate multiple heterogeneous AI Agents (Claude Code, Gemini CLI) to collaborate autonomously during offline hours. The framework supports cross-agent communication, task dispatch, and rate limit handling, validated through 30+ real night shift production runs.
What is MiroFish? An open-source multi-agent prediction engine with 16,000+ GitHub stars. It generates thousands of AI Agents that simulate community interactions to predict public opinion, market sentiment, and group behavior — essentially letting you predict anything through social simulation.