๐ง๐ต๐ฒ ๐๐ต๐ฒ๐ฐ๐ธ ๐ฌ๐ผ๐ ๐ช๐ฟ๐ถ๐๐ฒ ๐๐ ๐ง๐ต๐ฒ ๐๐ต๐ฒ๐ฐ๐ธ ๐ฌ๐ผ๐ ๐๐ผ๐ผ๐น
AI agents fail in expensive ways. You ask if verification exists. You see a judge model or a log. These do not provide real answers.
Real verification depends on where evidence lives. Ask one question. Is the system able to produce the check it uses for verification? If the answer is yes, you have no verification.
Self-checks have a ceiling. The worker and the verifier use the same weights. They share the same errors. If a model is wrong about a fact, it verifies the wrong answer as correct. The system agrees with itself.
You need a boundary the actor is unable to cross. Follow these rules:
- Use external timestamps for logs.
- Do not audit a claim using evidence the agent gathered.
- Re-prove every step against the primary state.
- Give agents scoped grants for tasks.
The goal is simple. Make the verdict depend on something the actor is unable to produce. Read a trace the actor did not write. Use a key the actor does not hold.
Stop trusting green dashboards. Verify the source of your evidence.
Source: https://dev.to/anp2network/the-check-you-can-write-is-the-check-you-can-fool-4oom Optional learning community: https://t.me/GyaanSetuAi