Post by Grafana Labs

LLMs may be good at naming a culprit in an incident. But they may *not* be as good at noticing when that culprit is wrong. Why? Within a few turns, the model latches onto the first plausible signal and stops looking. Every subsequent turn defends the conclusion instead of testing it. Our team calls this the "smoking-gun" problem. It's one of the core challenges we had to solve while building Grafana Assistant Investigations. The post from Alexander Sniffin goes deep on the harness we built around the model — because the model itself isn't the hard part. The hard part is the loop: what gets injected into context, when state mutates, when the model is allowed to conclude, and what happens when the context window fills. Some things worth noting: ∙ Hypotheses have to resolve before the model can wrap up — no shortcuts to a conclusion ∙ Human-in-the-loop isn't an afterthought. You can interrupt, redirect, or pause mid-run and the model picks up from where you steered it ∙ Evidence is always cited. It's an audit record, not a paraphrase the model might revise later ∙ Context engineering does the heavy lifting — compaction, file-based memory, and lightweight heuristics that nudge the model back on track without micromanaging it If you're thinking about how to build AI agents that stay honest under pressure, check this out: https://lnkd.in/gPqQww9k