Xingyao Wang Portfolio
CS PhD candidate at UIUC and co-founder/Chief AI Officer of All Hands AI; lead creator of OpenHands (formerly OpenDevin), an open platform for coding agents that interact with a computer the way a human developer would, and of CodeAct, an ICML 2024 paper arguing agents should express actions as executable code rather than fixed-format tool calls.
What makes it work
A breakdown of the choices that make this portfolio stand out.
A specific, arguable position on how agents should act
CodeAct's central claim — that letting an agent write executable code as its action space outperforms constraining it to predefined JSON tool calls — is a concrete architectural bet, not a neutral survey of options, and it changed how several later agent frameworks structure tool use.
A benchmark for the failure mode that matters most in practice
MINT specifically evaluates multi-turn interaction with tools and language feedback, targeting the realistic case where an agent must recover from an initial mistake using feedback, not just succeed on the first try.
A research lab and a company built around the same architecture
Founding All Hands AI to build OpenHands on top of the same research (CodeAct, SWE-Gym) shows a direct, visible line from the academic contribution to a fielded product, rather than the two being disconnected.
What AI Agent Architects can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Take a specific, defensible position on a core architecture question (like how agents should represent actions) rather than presenting a neutral menu of approaches.
Design your evaluations around realistic failure recovery, not just first-try success — it's a harder and more useful bar.
When your research produces a genuinely better architecture, consider building the product around it yourself rather than only publishing the finding.
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