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.
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