Agentic AI in the Wild: From Hallucinations to Reliable Autonomy

When we delegate tasks to AI agents—can we count on them to get it right? Agentic AI systems are increasingly stepping beyond static generation tasks into autonomous decision-making: scheduling meetings, booking travel, managing workflows, and assisting in scientific research. In these contexts, reliability is not just important—it is essential. Yet today’s foundation models remain prone to a critical failure mode: hallucination, where outputs are factually incorrect, semantically implausible, or detached from reality. While hallucinations are concerning in any generative system, these challenges are amplified in agentic settings, where models execute sequences of decisions without continuous human oversight.