Most APIs and tools are designed for and by the developers. AI agents don’t use them the way humans do. Small choices around tool granularity, naming, schemas, descriptions, outputs, and error handling can have surprisingly effects on how well an agent reasons and acts.

This talk looks at patterns that work and patterns that don’t when designing tools for AI agents. We’ll cover when to split or combine capabilities, how much logic should live inside a tool, how to return useful information without polluting context, how to design for failures and retries, and how to evaluate a given tool. The goal is to build tools that make agents reason, work and progress better.