🧩 Agent training environments are often surprisingly idealized: tools always respond cleanly, users always give clear instructions. But how well does an agent raised on that assumption hold up against real timeouts, errors, and sudden mid-task corrections from a real user? That's the question this paper takes on. The authors mined over 24,000 real tool failures across 557 MCP servers, distilling them into 8 failure families and 1,170 "recovery skills." They then teach an agent to reflect from three angles - recovering right after a failure, replanning from scratch, and responding to natural-language corrections from a user - and sharpen that ability further with reinforcement learning on a mix of noisy and clean rollouts. That's Agent-Reflex. The results back it up: average accuracy across 5 long-horizon tool-use benchmarks jumps from 31.6% for the base model to 58.6%, and even under injected noise the model holds at 45.9%. Baking the ability to recover from failure into the model's parameters, rather than relying on prompting, is what makes the difference. Title: Agent-Reflex: Teaching Language Agents to Act Reflectively URL: #LLMAgents# #ReinforcementLearning#