Agent "memory" that only learns from past trajectories can quietly bake in wrong knowledge. Here's a smart fix for that. Title: Grounding Agent Memory: Environment-Probing Curation for Enterprise Agents URL: ❓ What was wrong with traditional agent memory? 💡 Curating memory purely from completed trajectories means over-generalizing from a single lucky observation, and memories going stale the moment something like a database schema changes. ❓ How does this paper fix it? 💡 Before committing anything to memory, it lets the agent probe the live environment with read-only tools to verify the knowledge is actually correct, using a "propose-probe-commit" pipeline. ❓ How much does it actually help? 💡 On a database exploration benchmark, pass rate jumped from 39% to 73%, reward roughly 2.6x higher, while tool calls dropped 47% and cost fell about 50%. ❓ Is it practical for real deployments? 💡 Yes — it needs no model retraining and doesn't change the task-time interface, so it can be dropped into long-running enterprise agents as-is. #AIAgents# #AgentMemory#