Detect silent drift after 90 days
- Teams building artificial intelligence agents are warning that systems can get worse quietly as prompts, tools, models, and source data change over time. - The practical fix is routine evaluation: score answer quality, retrieval quality, factual accuracy, and citation support even when users are not complaining. - Observability vendors now pitch continuous testing for agents, not one-time launch checks. (langwatch.ai)
An artificial intelligence agent can keep returning answers with no outage, no crash, and no user complaint while its quality slips underneath. (langwatch.ai) That failure mode is usually called drift: the system still runs, but prompts change, retrieval results age, tools behave differently, or a model update nudges outputs off course. (dev.to) (evidentlyai.com) For retrieval-augmented generation, or RAG, the risk is simple: the agent answers from documents it can fetch, so stale or low-quality retrieval can make a fluent answer wrong. Evidently says teams often monitor retrieval quality, answer accuracy, and refusal behavior as separate signals. (evidentlyai.com) The same problem shows up in multi-step agents. LangWatch argues that infrastructure metrics like latency, token use, and HTTP 200 responses do not show whether the content is accurate or grounded. (langwatch.ai) That is why the current advice is to instrument the agent itself. Langfuse says teams should capture traces, attach evaluation scores, and measure factual accuracy, completeness, and other output dimensions over time. (langfuse.com 1) (langfuse.com 2) Citation support has become one of the clearest checks for research-style agents. If an answer cites sources that do not back the claim, the system may look polished while the underlying grounding has already failed. (langfuse.com) (langwatch.ai) The push now is toward longitudinal evaluation, which means testing the same workflows repeatedly instead of grading a single demo run. LangWatch describes simulations and experiments as a way to catch regressions after prompt edits, model swaps, and retrieval changes. (langwatch.ai 1) (langwatch.ai 2) Evidently makes the same point in plainer terms: continuous testing catches drift and emerging risks early. In other words, a quiet agent is not necessarily a healthy one. (evidentlyai.com)
