Jiachen Bai
Head of the Intelligent R&D Center at TiDB and a senior engineer
Jiachen Bai is Head of the Intelligent R&D Center at PingCAP (TiDB) and a Senior Engineer. He has long focused on the intersection of artificial intelligence and software engineering. He currently leads product development and team building in intelligent R&D, with a focus on applying large language models and agent technologies to enterprise software development. Previously, he worked at Amazon Web Services (AWS) and a Silicon Valley autonomous driving company, where he gained extensive experience in distributed systems, cloud infrastructure, and large-scale engineering. He later served as a Founding Engineer at a startup, where he built TabbyML, an open-source AI coding agent, from the ground up. The project has gained more than 30,000 stars in the global open-source community and has become a widely recognized open-source project in the AI coding agent space. He has extensive hands-on experience and deep insights into agent architecture, AI-native software development paradigms, and enterprise AI application deployment. He is committed to exploring how AI can evolve from a personal productivity tool into a core source of productivity at the organizational level.
Topic
From Individual Productivity to Organizational Intelligence: Scaling Agent Capabilities Across the Enterprise
Most enterprise agent applications today remain focused on individual productivity. AI capabilities are difficult to coordinate across roles and workflows, and even harder to consolidate into reusable organizational assets. PingCAP Loop addresses this challenge through a multi-agent collaboration platform that integrates AI into organizational operations in a role-based and standardized manner. By systematically capturing project knowledge, business rules, and execution records, Loop helps transform fragmented individual productivity gains into organization-wide intelligence. Drawing on real-world customer cases, this talk will break down the complete journey from individual AI adoption to organization-level AI productivity transformation, and explore practical approaches for building agent-native organizational capabilities. Agenda Challenges in Enterprise Agent Adoption Adapting to organizational permissions and workflows Governing enterprise data and operational practices Bridging individual capabilities and organizational capabilities PingCAP's Approach: An Organization-Level Agent Platform A new paradigm for agent-native organizations Introduction to the Loop platform FDE Practice Co-building agent-native organizations with customers Learning from customers: Closing the loop between requirements, feedback, and product development The Future of Enterprise Agentic Organizations Key Takeaways Understand the key gap between individual AI capabilities and organizational AI capabilities, as well as the real bottlenecks in enterprise AI transformation. Learn about the core design principles and implementation methodology of PingCAP Loop, a multi-agent collaboration platform. Gain practical insights into the complete journey of scaling AI productivity at the organizational level through real-world customer cases. Take away a reusable framework for planning the transition from individual productivity gains to organization-wide intelligence.
