I Tracked Every AI Hallucination for a Week — The Numbers Were Worse Than I Thought (1787914341254)

Last week I ran an experiment. Every time my AI agent generated an output, I verified it manually and logged whether it was correct. The results were embarrassing. Out of 200 outputs across Claude, GPT, and DeepSeek: 36 were confidently wrong (18%) 12 fabricated citations or references 8 tried to use tools with hallucinated arguments 4 leaked system prompt content That's nearly a fifth of my token budget going to outputs I had to manually catch and redo. Why this happens LLMs are optimized to sound convincing, not to be correct. When they hit uncertainty, they fill gaps with plausible-looking content. The problem is that plausible != true, and in code, "plausible but wrong" costs hours to debug. What I built A verification layer that sits between the model and your workspace. It runs after generation but before the output reaches your codebase: Citation checker — validates references against actual sources Code validator — checks syntax and logical consistency Safety leak detector — catches leaked system prompts Argument verifier — checks tool call parameters against schemas Coherence scorer — compares output against the original prompt All runs in under 100ms on CPU. Model-agnostic. Free. Download: https://agent-download-site.vercel.app Try auditing your own agent's outputs for a day. You might be surprised what you find.