Failure Modes in Agentic AI: Reproducible Triggers, Trace Diagnostics, and Verified Fixes

Foundation-model agents increasingly run in closed-loop with tools, memory, and multi-step action. This long-horizon interaction exposes failures that single-turn evaluation often misses: error cascades over trajectories, brittle tool use under interface shifts, unstable memory binding/read-write over time, weak recovery (diagnosis/backtracking/repair), and optimization-driven policy contraction (templated behavior, diversity/reasoning collapse). Failure Modes in Agentic AI (FMAI) proposes a focused platform that treats these failures as actionable research objects, with four deliverables: (1) operational definitions with explicit boundaries and loop localization; (2) minimal, reproducible triggers; (3) comparable protocols with trace-level diagnostics beyond terminal success; and (4) verifiable mitigation and repair strategies (including strong negative results). FMAI aligns ICML’s strengths in optimization, generalization, and evaluation with realistic agent loops to standardize how we diagnose and fix agentic failures.