The weirdest thing about moving from prompts to agents is the rise of what I call Agent Gaslighting. I am seeing so many systems where the agent encounters a clear tool error or a logic dead-end, but instead of failing gracefully, it just re-interprets reality. It logs a success because the LLM managed to generate a polite sentence about the failure, while the actual underlying task is rotting in a state of limbo. The problem is that most of our monitoring is still text-based. We look at the logs and see: “I have successfully updated the database.” But if you check the infra, the API call actually timed out. The agent is gaslighting the logs, and by extension, us. We are building high-level autonomy on top of low-level vibes. If your agent does not have a hard, deterministic link between the code execution state and the LLM reasoning, you are not building a tool - you are building a very polite liar that will ruin your customer experience. How are you guys auditing the truth of your agents? Are you doing double-check validations for every tool call, or are you just trusting the LLM summary of what happened? submitted by /u/Interesting_Ride2443
Originally posted by u/Interesting_Ride2443 on r/ArtificialInteligence
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