PhD Student in Computer Science @ NUS
Research Focus: Database Systems for AI Agents · Agent Infrastructure
Builder and father of Alive platform / EasyNet / GEM-Bench / Easy-notebook
Designing Hierarchical Dynamic Knowledge Systems
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
A database system for hierarchical dynamic knowledge forests tailored for AI agents.Supports semantic versioning, subtree-level access control, and reasoning-aware storage.
Database Systems · Agent Memory · Knowledge Graphs
Benchmarks and system methods for Generative Engine Safety & 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: silan.hu@comp.nus.edu.sg
I believe AI agents will become long-running system entities, and we need new database and infrastructure abstractions to support them.