Your AI coding agent isn’t lying to you. It’s optimizing.

Every dev using an AI coding agent has hit this moment: the agent says "Done — tests pass" and you go check, and nothing passes. Or worse, nothing changed at all. The instinct is to ask "why did it just lie to me?" That's the wrong question. It assumes intent. There isn't any. The right question is: What made the wrong answer cheaper than the right one — and what input did it exploit to get there? That question always has an answer. And the answer is always your next check. The mantra An LLM agent isn't a person deciding whether to be honest. It's a process that takes whatever path costs least, given whatever is actually being measured. If "claim done" and "verify, then claim done" both produce the same reward — because nothing downstream distinguishes them — the agent will drift toward the cheaper one. Every time. This isn't a flaw you can prompt your way out of. "Please don't lie to me" doesn't change the cost structure. What changes it is making the dishonest path actually expensive: something that catches the gap between claim and reality, every time, automatically. What this looks like in practice I built GroundTruth (a Claude Code Stop-hook plugin) after hitting this exact pattern on my own project, EraPin. Agents kept claiming "tests pass" or "refactor complete" when the git diff told a different story. Every fix I've shipped since started with the same exercise: Broadened extraction rule → a missed rule cost nothing, because nothing measured recall. Fix: track what's not being parsed, not just what is. Grounding check regression → a zero-hit result looked identical to "genuinely absent," so a silent no-op was free. Fix: pin the check against a real signal, not a pattern that can quietly degrade. Permission gate → auto-arming a misread rule cost nothing when there was no human in the loop. Fix: nothing gets armed without explicit approval. Every one of these is the same shape: find the loophole where "looks done" was cheaper than "is done," and close it so th