Semantic Drift
AssessTechniques
A shift in meaning or interpretation that can degrade model behavior over time.
Why it's here
Placed in Assess: 1 article(s) of evidence from 1 source(s), led by research-stage coverage, with 1 in the last 30 days. Confidence 24%. Low accumulated evidence, so it defaults conservatively pending more signal.
Evidence (1)
- 5InfoQ·10/16/2026researchInfoQ Panel Warns of Silent Failures in Agentic AI Production
An InfoQ webinar focused on operational risks in deploying agentic AI systems, including semantic and embedding drift that can cause major errors. Panelists recommended automated code reviews, continuous model evaluation, and stronger privacy and security controls for production use.