📍 Shanghai / Suzhou | 💼 SOFTWARE ENGINEER II @ Microsoft

Exploring how agent engineering, backend systems, and strong engineering foundations come together in real-world AI products.

I’m a software engineer at Microsoft working on AI-powered product and platform experiences. My work sits at the intersection of agent systems, backend infrastructure, and production engineering, with a focus on making AI capabilities practical, reliable, and ready for real users.

• Building AI systems in production - working on product and platform capabilities that bring AI into real engineering environments
• Exploring agent engineering - learning how runtime loops, tool orchestration, execution control, and completion flows work in complex AI systems
• Strengthening backend foundations - building around APIs, storage, caching, service coordination, and stateful infrastructure
• Improving engineering quality - investing in telemetry, observability, debugging, throttling, release workflows, and operational readiness

• Societas - worked across agent workflows, tool execution patterns, sandboxed environments, telemetry, distributed coordination, and backend architecture for AI-driven product experiences
• Office Plus - supported backend development, service operations, development workflows, and production-oriented engineering practices
• PCM - gained hands-on experience in configuration systems, deployment pipelines, release processes, environment management, and large-scale service debugging

I’m gradually building and sharing more of my work here. This section will grow over time as I publish projects and experiments.

• Coming soon - AI engineering experiments
• Coming soon - agent system prototypes
• Coming soon - backend and platform tools

• AI Engineering - turning model capabilities into product features that are useful beyond demos
• Agent Systems - understanding how tools, prompts, execution state, and multi-step reasoning come together in real applications
• Backend Infrastructure - designing and supporting the systems that make AI applications stable and scalable
• Engineering Excellence - building the habits and foundations that help systems survive production reality

• Agent engineering and tool-use workflows
• LLM application architecture
• AI coding workflows and developer productivity
• Observability, tracing, and system reliability for AI products

笨鸟先飞，量变产生质变。

The early bird gets ahead. Consistent effort compounds, and quantitative change eventually leads to qualitative change.

• GitHub: @tomqiaozc
• LinkedIn: coming soon
• Blog / Notes: coming soon