Teng Li
ML Engineer @ Zoom · Seattle
I build AI agent systems for production — and write about the engineering underneath. Notes on ML systems, agent tooling, and a career spent closing the gap between what works in a demo and what survives real users.
What I write about
ML & Engineering
Building and running ML systems in production — model serving, data pipelines, and the unglamorous infrastructure that keeps things working.
AI Agents & MCP
Field notes from shipping agents to real users: tool design, connectors, MCP servers, eval harnesses, and the gap between spec-compliant and actually usable.
Career & Industry
The longer view — how ML engineering careers compound, what's changing in the industry, and lessons from the inside of a fast-moving field.
Recent writing
All posts →-
My eval said a perfect MCP server was broken. It was the eval that was lying.
AgentsMCPEngineering
Three rounds of calibration, four real servers, $0.60 in API costs — how I made an LLM-powered tool-selection benchmark fair enough to publish, and what it revealed about static lint rules predicting live model behavior.
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I lint-scanned 36 popular MCP servers. A third of them are failing your agent.
AgentsMCPEngineering
Your MCP server can be 100% spec-compliant and still be unusable by an agent. I built mcpgrade to measure the difference — then pointed it at 36 popular servers.
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Designing tools LLMs can actually use: five failure modes I keep seeing
AgentsEngineering
Your tool API can be perfectly engineered and still confuse every model that touches it. Field notes on why, from someone who integrates agent tools for a living.
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Why this blog exists
Industry
Production AI agents fail in ways that never show up in demos. Someone should write those down.
Projects
