Most developers who build AI agents make the same mistake: they spend weeks designing the orchestration layer, tuning the system prompt, and picking the right model — then hand the LLM a pile of hastily wrapped API endpoints and wonder why it fails in production. Here's the hard truth from teams shipping agents daily: tool design has a larger impact on agent reliability than prompt engineering. A well-crafted tool prevents hallucinations at the structural level. A poorly crafted tool guarantees them. This article walks through what we've learned from building, deploying, and debugging production AI agents across dozens of real-world workflows. You'll get concrete patterns, working code examples, and the anti-patterns that cost us the most in production incidents. The Contract Between Deterministic and Non-Deterministic Code When you write a function for another developer, you're working between two deterministic systems. Same input, same output. The calling code knows exactly what to expect.…