AI Agent applications
Agent workflows, event contracts, evidence review, human approval, and transparent degradation paths for unreliable external model runs.
Candidate file · 2026
Zhao Yanbing
AI Agent / LLM Application Engineering Intern
I turn ideas into inspectable, runnable work, with a focus on AI Agents, LLM applications, and full-stack engineering.
Selected profile
Engineering work with evidence.
Full-stack systems
TypeScript, React/Next.js, Go, Python, PostgreSQL, SSE/WebSocket, Docker, continuous checks, and end-to-end regression practice.
Public project evidence
Agent Arena, TrustOps, and EcoMatrix connect implementation to tests, documentation, project source, and explicitly stated limits.
Strengths
How I approach building.
- Turns ideas into runnable work, with code, tests, demos, or project documentation retained for core projects; unfinished work is documented with its reason and next step.
- Has experience in module decomposition, interface integration, and code collaboration; defines state, messages, and responsibility boundaries before connecting pages and model calls in multi-agent systems.
- Maintains open-source AI applications and learning products; uses competition awards and public work to test engineering implementation.
Original file
Read the full Chinese résumé.
The official two-page PDF contains complete education, skills, internship experience, project records, competition honours, and direct links.