Building AI tools that work reliably in production requires careful design decisions beyond just calling an LLM. This article explores three critical aspects: tool granularity, parameter management, and error recovery. Choosing the right granularity for a tool function—whether to expose fine-grained operations or coarse, high-level actions—directly impacts how well an LLM can use it. Parameter design involves defining clear, well-typed inputs that the model can generate correctly, while error recovery ensures the system gracefully handles failures and retries. These patterns are essential for developers creating AI agents, copilots, and automation tools. By focusing on these design principles, teams can build more robust and user-friendly AI-powered features that perform consistently in real-world scenarios.
Key design patterns for AI tools: choosing granularity, managing parameters, and handling errors to build reliable LLM applications.