Built a tiny library after our AI confidently lied about a file that didn't exist
A few weeks back our internal AI CTO assistant told me it had
fixed a bug in a specific file — fake path, fake line number, said
with total confidence. That file never existed.
Turned out prompting alone doesn't reliably catch this, and having
a second AI review the first doesn't fully fix it either — same-
family models share blind spots.
So we built two small deterministic layers that sit outside the
model itself: a regex-based grounding check (no LLM call needed)
and a cross-family adversarial review with a hard guard against
the reviewer hallucinating its own flags.
Just open-sourced it — ~500 lines, zero deps, MIT.
github.com/polarisbuiltinc-wq/ora-grounding
Curious if others building with LLM agents have hit the same
"confidently wrong" problem, and how you're handling it.
Replies
One more detail I didn't fit in the post — the trickiest part
wasn't catching the fabrication itself, it was making sure the
reviewer (the second AI checking the first) couldn't also just
hallucinate a false flag. We ended up requiring every flag to quote
the draft verbatim before we'd trust it — if the quote doesn't
match character-for-character, we drop the flag and log it as a
reviewer error instead.
Felt like a dumb-simple fix in hindsight, but we didn't think of it
until we caught the reviewer doing exactly that in production.
Anyone else running multi-agent review setups hit this? Curious if
there's a more elegant way to handle it than string-matching.