Most teams obsess over which LLM to pick, but the real leverage is in how you structure the work around it. A poorly written prompt or tool description can silently tank agent accuracy by up to 66%, and most people don't even realize it's happening. The fix isn't more compute or a bigger model. It's better orchestration. Three separate papers have now shown that orchestration design matters more than model selection by roughly an order of magnitude when it comes to token cost. Graph-structured context beats flat text. Clear tool descriptions matter more than fancy prompting. These aren't subtle effects. I spent last month auditing my own agent setups and found half a dozen silent accuracy killers hiding in plain sight, mostly in how tools were described to the model. You can catch the same issues in under an hour if you know what to look for. The Hidden Accuracy Tax Tool descriptions aren't documentation—they're the interface contract between you...