Meta AI 使用第二个 AI 智能体作为记忆教练，让长任务保持正轨

During long tasks, AI agents often forget constraints, repeat failed commands, and rediscover errors they've already diagnosed. Meta AI's proposed memory module tracks that information and decides when to remind them. In a new paper, Meta AI researchers describe how these failures play out. An agent recognizes a constraint early on but later violates it while fixing an unrelated bug. It sees that a command failed, then tries a nearly identical version soon afterward. It diagnoses an error pattern but later treats the same pattern as new. The authors call this "behavioral state decay." The state that guides the agent's decisions gets scattered across a growing task history. It can become buried deep in the context window or fall out of it altogether. Even when the information remains in the transcript or context window, it may no longer reliably shape the agent's behavior. Meta AI says simply giving agents access to longer histories doesn't solve the problem. Existing memory systems focus on storing, updating, and retrieving information. According to the paper, that works well for personalization and recall across sessions. But agents working through a task face another problem. The system must decide when a memory is useful enough to bring back. Too few reminders lead to repeated mistakes, while too many add latency, consume tokens, and distract the agent from its current work. This goes beyond summarization: a summarizer just decides what information to retain where Meta's system decides whether a stored execution state should influence the agent's next move. Since failure modes vary widely by task, a fixed summarization rule can't make that call reliably. The proposed system pairs an unmodified "action agent" with a separate "memory agent." At fixed intervals, the memory agent reviews a sliding window of the most recent steps and updates a structured memory bank. It then decides whether to add a brief reminder to the action agent's next call or stay silent. The authors say the module can work with existing agents and harnesses as a plug-and-play component. Unlike a general advisor model, it provides only memory-based reminders and doesn't offer broader strategic advice. The memory bank has three sections. A private status field tracks progress and outstanding risks and is never shown to the Action Agent. Knowledge Memory stores stable facts such as requirements, file paths, and configurations. Procedural Memory records what the agent tried and what happened, including failed commands, successful fixes, and rejected hypotheses. During each memory step, the agent can update the bank only through predefined tool calls rather than freely overwriting its contents. It then decides whether to reactivate a stored state and, if needed, writes a targeted reminder. Choosing not to intervene is part of the policy. The researchers tested the system on Terminal-Bench 2.0, which evaluates autonomous agents in realistic command-line environments. They also used tau2-Bench, which tests conversational tool use in the airline, retail, and telecom sectors. Claude Opus 4.6 served as the memory agent, though the model has since received several updates. With the older Claude Sonnet 4.5 as the action agent, the system solved 46 percent of Terminal-Bench tasks on its first attempt. The baseline solved 38 percent. On tau2-Bench, the task-weighted average rose from 55 to 62 percent. Results varied by domain. Scores for airline and retail tasks each rose by about 10 percentage points, while telecom improved by only 3 points. The researchers say the uneven gains suggest that the memory agent intervenes at different rates depending on the task rather than applying a fixed aggregation rule.