AI agents are increasingly used in long, multi-turn workflows in both research and enterprise settings. As interactions grow, agent behavior often degrades due to loss of constraint focus, error accumulation, and memory-induced drift. This problem is especially visible in real-world deployments where context evolves, distractions are introduced, and decisions must remain consistent over time. A common practice is to equip agents with persistent memory through transcript replay or retrieval-based mechanisms. While convenient, these approaches introduce unbounded context growth and are vulnerable to noisy recall and memory poisoning, leading to unstable behavior and increased drift. In this work, we introduce the Agent Cognitive Compressor (ACC), a bio-inspired memory controller that replaces transcript replay with a bounded internal state updated online at each turn. ACC separates artifact recall from state commitment, enabling stable conditioning while preventing unverified content from becoming persistent memory. We evaluate ACC using an agent-judge-driven live evaluation framework that measures both task outcomes and memory-driven anomalies across extended interactions. Across scenarios spanning IT operations, cybersecurity response, and healthcare workflows, ACC consistently maintains bounded memory and exhibits more stable multi-turn behavior, with significantly lower hallucination and drift than transcript replay and retrieval-based agents. These results show that cognitive compression provides a practical and effective foundation for reliable memory control in long-horizon AI agents.

翻译：AI智能体在研究和企业环境中越来越多地应用于长流程、多轮次的工作流。随着交互的增长，智能体行为常因约束焦点丧失、错误累积和记忆诱导漂移而退化。这一问题在现实世界部署中尤为明显，因为上下文会动态演变、干扰因素不断引入，且决策需随时间保持一致性。当前普遍做法是通过对话记录回放或基于检索的机制为智能体配备持久记忆。这些方法虽便捷，却会导致上下文无限增长，并易受噪声回忆和记忆污染的影响，从而引发行为不稳定和漂移加剧。本研究提出一种受生物学启发的记忆控制器——智能体认知压缩器（ACC），它用有限内部状态替代对话记录回放，并在每轮交互中实时更新。ACC将信息回溯与状态确认分离，在实现稳定条件约束的同时，防止未经验证的内容转化为持久记忆。我们采用智能体-评判驱动的实时评估框架对ACC进行测试，该框架同时衡量任务结果和长程交互中的记忆驱动异常。在涵盖IT运维、网络安全响应和医疗工作流的多种场景中，ACC始终保持有限记忆，并展现出更稳定的多轮次行为，其幻觉率和漂移率显著低于基于对话记录回放和检索机制的智能体。这些结果表明，认知压缩为长周期AI智能体的可靠记忆控制提供了实用且有效的基础。