Shunyu Yao Portfolio
Researcher at OpenAI who authored ReAct (reasoning + acting, ICLR 2023) and Tree of Thoughts (NeurIPS 2023), two of the most widely adopted frameworks for how language-model agents plan and use tools; also co-created SWE-agent/SWE-bench and the tau-bench benchmark for evaluating tool-agent-user interactions.
What makes it work
A breakdown of the choices that make this portfolio stand out.
A named reasoning pattern that became a default agent architecture
ReAct's interleaved "thought, action, observation" loop is now the default scaffold underneath most production agent frameworks, not just an academic curiosity — a rare case of a research paper's architecture becoming industry-standard implementation.
Benchmarks built alongside the architectures they test
WebShop, SWE-bench, and tau-bench each give the field a way to actually measure whether an agent design works, rather than judging it anecdotally — pairing new agent patterns with the yardstick to evaluate them is more useful than either alone.
A body of work that moves in step with agent capability itself
The progression from ReAct through Tree of Thoughts to Computer-Using Agent tracks agents evolving from single tool calls to multi-step deliberation to full computer control, showing sustained focus on one research question as it developed.
What AI Agent Architects can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
If you design a reasoning or planning pattern for agents, name it clearly and explain the loop precisely — a well-named pattern is what gets adopted into other people's frameworks.
Build the benchmark alongside the architecture, not after — a way to measure whether your design actually works is as valuable as the design itself.
Let your public work show a single research question evolving over time rather than a scattered set of unrelated demos.
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