PhD Student in Computer Science @ NUS
Research Focus: Database Systems for AI Agents · Agent Infrastructure
Creator of Alive, EasyNet, GEM-Bench, and Easy-Notebook
I believe AI should be accessible to everyone—not just experts.
My goal is to make AI easier to discover, easier to use, easier to manage, easier to organize, easier to protect, easier to govern, and easier to monetize.
I am interested in building system-level foundations for AI agents, especially at the intersection of:
- Database systems for AI agents
- Hierarchical & dynamic knowledge structures
- Agent behavior versioning, reuse, and evolution
- Agent execution infrastructure & distributed systems
My current research direction focuses on designing AI-native database systems that support:
- evolving agent knowledge,
- traceable reasoning paths,
- and reusable agent behaviors as first-class system assets.
A distributed execution infrastructure for AI agents and functions.Designed to support language-agnostic agent behaviors, privacy-first compute sharing, and agent-level orchestration.
Python · Go · gRPC · Distributed Systems
Benchmarks and system methods for Generative Engine Marketing,studying how LLM-generated answers interact with visibility, sponsorship, and user trust.
Evaluation · Benchmarking · Agent Alignment
- Languages: Python, Go, Rust, TypeScript
- Systems: Databases, Distributed Systems, Agent Infrastructure
- AI: LLM Agents, Planning, Reinforcement Learning
- Tools: React, Tauri, gRPC, Docker
- GitHub: https://github.com/Qingbolan
- Email: [email protected]
I believe AI agents will become long-running system entities, and we need new database and infrastructure abstractions to support them.