"My Agent Refused 96 Times": Building Self-Editing Agents with Hard Failure Modes
Originally published on tamiz.pro . In the early days of shipping LLM-based agents, we optimized for output volume. If the model could not find the answer, it often generated a plausible one anyway. This is the "yes-man" problem. In critical systems—financial auditing, code generation, or compliance checks—this creates a dangerous class of errors: confident hallucinations . Recently, a senior ML…
In the early days of developing AI agents powered by large language models (LLMs), the primary focus was on generating as much output as possible. However, this "yes-man" approach often led to inaccurate responses, particularly in critical systems like financial auditing, code generation, and compliance checks. These systems would sometimes provide confident but incorrect information, known as hallucinations, which could have serious consequences.
Recently, a senior ML engineer shared an unsettling benchmark result showing that their production support agent refused to answer a valid question 96 times out of a test set. This was not a failure rate, but rather a demonstration of the agent's hard failure mode, where it correctly identified that the retrieved context was insufficient or contradictory.
This marked a significant shift in how autonomous systems should be designed, moving from probabilistic output to deterministic verification.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
