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AI 时代的移动端稳定性保障 —— 柯南 AI Agent 实践｜QCon上海
AI InsightThe popularization of AI Coding is shifting the engineering bottleneck from code generation to downstream stability assurance. Kuaishou's practice with the 'Conan AI Agent' indicates that LLM applications in software engineering are expanding from code creation to complex operational maintenance and risk governance.Key TakeawayAI applications in software engineering are extending from frontend code generation to backend stability assurance.Why It MattersTraditional downstream engineering heavily relies on manual labor, failing to match the throughput driven by AI Coding. Using agents to solve stability issues in multi-platform environments is key to completing the AI-driven development lifecycle.Who's Affected- BeneficiaryMobile DevelopersAI agents are expected to take over complex cross-platform exception debugging and stability governance.
- WatchingSoftware Engineering Tool VendorsDownstream workflow automation is becoming a new competitive frontier for AI development tools.
What's NextFuture observation should focus on practical deployment cases and automated metrics of AI agents handling cross-tech-stack anomalies in complex engineering environments.Importance 60/100DataAgent - 快手大数据生产与分析的智能化探索之路｜QCon上海
AI InsightKuaishou's deployment of an enterprise-grade DataAgent for big data production and analysis reflects the shift of LLM applications from general chat to domain-specific workflow re-engineering. This exploration indicates that solving multi-agent collaboration and domain knowledge accuracy is the key breakthrough for enterprise AI for Data adoption.Key TakeawayEnterprise data analysis is shifting from traditional tool-driven to multi-agent collaborative intelligent-driven paradigms.Why It MattersBig data production and analysis involve high-density logic and strict security requirements. Exploring the deployment of multi-agents across the data lifecycle provides a reusable architectural reference for LLM engineering in complex industrial scenarios.Who's Affected- BeneficiaryData AnalystsAI agents assisting with data retrieval, extraction, and AB testing lower the barrier to data consumption and boost efficiency.
- WatchingAI Infra DevelopersThe parent-child Agent + Skill architecture offers a referenceable engineering practice for multi-agent collaboration and reliability.
What's NextFuture observations should focus on DataAgent's throughput performance, accuracy metrics under multi-agent collaborative architecture, and weekly active user retention in actual production environments.Importance 55/100生成即合规：Agentic AIGC 驱动的广告素材修复实践｜QCon上海
AI InsightKuaishou's application of Agentic AIGC to ad creative repair indicates that generative AI applications in vertical commercial scenarios are evolving from simple content generation to automated governance focusing on both compliance and marketing optimization.Key TakeawayAd content governance is shifting from manual review and editing to automated remediation and multi-objective optimization driven by Agentic AIGC.Why It MattersTraditional ad review pipelines are lengthy and often lead merchants to weaken marketing expressions. Implementing agentic architecture for precise local remediation and multi-objective reinforcement learning helps balance safety with commercial conversion efficiency.Who's Affected- BeneficiaryKuaishouImproving ad creative remediation efficiency via Agentic AIGC may lower content governance costs and enhance merchant conversion rates.
- BeneficiaryAdvertisersAutomated material repair lowers compliance barriers, helping maintain marketing appeal while mitigating risks.
What's NextSubsequent attention should be paid to the specific architectural details shared at QCon Shanghai and the practical deployment results under tens of millions of creative throughputs.Importance 45/100