Barry Zhang

Don't Build Agents, Build Skills Instead – Barry Zhang & Mahesh Murag, Anthropic

Dec 8, 2025 · 16:22

Barry Zhang and Mahesh Murag of Anthropic argue that instead of building domain-specific agents, developers should build reusable Skills—organized folders of files that package procedural knowledge for agents. They explain that skills are progressively disclosed to protect the context window, use scripts as self-documenting tools, and have already grown to thousands in five weeks, including foundational, partner, and enterprise skills. Skills complement MCP servers by providing expertise while MCP handles connectivity. The future includes treating skills like software with testing and versioning, and enabling agents to create their own skills for continuous learning, ultimately creating a collective knowledge base that makes agents more capable and reliable.

How We Build Effective Agents: Barry Zhang, Anthropic

Apr 4, 2025 · 15:09

Barry Zhang of Anthropic's Applied AI team argues that effective agents require simplicity, not complexity, and should be built only for tasks with high value and ambiguous problem spaces. He offers a checklist: ensure task complexity is high, value justifies token cost, critical capabilities are de-risked, and errors are easily discovered (e.g., coding with unit tests). Agents are just models using tools in a loop — environment, tools, and system prompt — and he advises iterating on these three components before optimizing. To improve agents, developers should think like them by narrowing their perspective to the agent's 10-20k token context window and even asking Claude to critique its own tools and trajectories. Zhang forecasts three open challenges: making agents budget-aware by enforcing time/token/money limits, enabling self-evolving tools via meta-tools, and building asynchronous multi-agent communication beyond synchronous turns.