A key challenge in building production-grade AI agents is ensuring they call the right tools with the correct parameters. This article from a Chinese developer blog provides a structured approach to designing input and output contracts for agent tools. The input contract defines the tool name, description, parameter schema, and usage boundaries, helping the model select and invoke tools accurately. The output contract specifies structured results, status codes, and error feedback, enabling the agent to make reliable decisions based on tool responses. Together, these contracts create a stable 'think-act-judge' loop, improving system reliability and debuggability. For developers working on agent frameworks or integrating LLMs with external APIs, this design pattern is essential. The article offers concrete examples and best practices, making it a valuable reference for both beginners and experienced engineers.
A practical guide to input/output contract design for agent tool calling, ensuring reliable and debuggable AI systems.