Engineer Cuts AI Agent Test Runs from 2,490 to 206 While Maintaining Full Coverage

A developer working on the open-source project agent-tooltrust reduced a planned test matrix of 2,490 LLM-based runs to just 206 by rethinking what each test actually needed to prove. The original cross-product approach of 83 agents multiplied by 30 scenarios would have taken roughly 12 days and generated over 10,000 real model calls. The engineer identified that the core engine was already validated through deterministic tests, meaning live LLM calls were only necessary to confirm that each framework could correctly surface key decision types in a real agent loop. By splitting the remaining tests into two targeted plans — one covering scenario breadth and another covering per-framework decision depth — the same coverage was achieved in a single afternoon. The approach highlights how combinatorial test matrices, while easy to defend in meetings, often re-prove behavior that cheaper deterministic methods have already established.