Why Your AI Agent Fails: The Math You Need to Know

Original author：Rahul Pandey

Original source：https://www.youtube.com/embed/sMMNXTmimJg

Every step in this AI agent works at least 90% of the time. It still fails more than one in three invoices. In this video I simulated a 12-tool invoice agent from scratch in plain Python (no LLM, no framework) and simulate 10,000 runs to find out why agents fail even when every tool works 90% of the time. We cover the multiplication law, the two independence lies that break it (bad days and silent state poisoning), what actually fixes it, and what the fix costs. What you'll see: - Tools averaging 96% reliable still lose 371 of every 1,000 invoices - One tool (PDF extraction) causes about a quarter of all failures - On a "bad day", success drops to under 9% - Retries take success from 63% to 94%, but make silent wrong answers MORE likely - Adding a verifier: 97% correct, 25x fewer wrong ledger entries, for +0.7 tool calls per good invoice Chapters: 00:00 Introduction 00:24 Agentic workflows vs agents 04:15 The perfect-world agent simulation 06:12 Tools become coin flips 10:27 Ten thousand invoices simulation 11:32 The multiplication law 21:18 Where invoices fail 27:00 Lie #1: bad days simulation (correlated failures) 31:25 Lie #2: silent state poisoning 37:58 Fighting back: retries and verifiers 40:58 Conclusion #AIAgents #AgenticAI #BuildFromScratch #Agents #AI