Talk Title: Toward Reliable Deployment of LLM Agents: From Uncertainty Quantification to Progress Advantage

LLM agents are increasingly deployed on long-horizon tasks with tool use, irreversible actions, and unpredictable feedback. Yet we have few principled ways to tell, mid-episode, whether an agent is on track or quietly failing. Most uncertainty quantification (UQ) research still centers on single-turn QA, a poor match for interactive agents. In this talk, I'll present a general formulation of agent UQ and the challenges unique to agentic settings, from choosing uncertainty estimators to modeling how uncertainty evolves over an interaction. I'll then show that a powerful answer has been hiding in plain sight: RL post-training already yields an implicit step-level signal, the progress advantage, which recovers the optimal advantage function with no annotation or reward-model training. Across test-time scaling, UQ, and failure attribution, this free byproduct beats confidence baselines and even dedicated trained reward models.