Wang Jianjun
Kuaishou · Operations Tech

Kuaishou · Operations Tech

Main Site Technology (Operations Tech) · Agent Developer · 2026.08–Present

Leading evaluation benchmarks, Figma JSX declarative structured editing, and MCP integration for M0 Design (PRD → Figma → Code) Agent: connecting generation, evaluation, and production invocation into a closed engineering loop.

Engineering Focus

Core Pillars

Agent Eval & Data Flywheel

Led 84-task / 160-page / 979-atomic-criteria real-world benchmark; combined native Figma parsing, screenshots, and Rule/LLM-as-a-Judge with automated trace root-cause attribution, achieving 95%+ core acceptance coverage.

Agent EvalBenchmarkTrace AttributionData Flywheel

Figma Declarative Structured Editing

Serialized Figma SceneGraph into JSX structures for model read/write with safe Reconcile diff writebacks; 95%+ complex design edit success rate on 143+ test cases, reducing fine-grained tool calls by 60%+.

SceneGraph to JSXReconcile DiffComponent PropsTool Call Reduction

MCP Integration & Concurrency Stability

Packaged generation capabilities into MCP for internal agents (Agent → MCP → Figma Runtime / Host); resolved write timeouts and runtime crashes, raising complex generation E2E success rate to 95%+ and halving concurrency errors.

MCP IntegrationConcurrencyRuntime RecoveryE2E Reliability

System Design Principles

Methodology
01

Define delivery before generation

An output needs a clear target identity, editable structure and constraints; a successful tool call is not a delivery verdict.

02

Separate evidence from judgment

Figma AST snapshots, visual screenshots, and semantic requirements each have distinct evidence sources. When evidence is insufficient, uncertainty stays explicit.

03

Make iteration a closed loop

Build a continuous loop: Online Cases → Eval → Root-cause Attribution → Fixes Merged → Regression → Dataset Re-injection.

Explore Engineering Principles

Read the engineering methodology on verifiable agent delivery

Read Delivery & Eval Methodology