MIT Technology Review has published a new analysis of confidence in AI agents, looking at how these systems behave near the edge of their technical capability. The piece focuses on a core deployment problem: agents can sound certain even when their execution is brittle.
That distinction matters as companies move agents from demos into workflows where mistakes have operational consequences. Confidence calibration, uncertainty reporting and verification become practical requirements, not academic extras.
The broader takeaway is that agent progress should be judged by reliable task completion under pressure, not by polished responses in controlled examples.