AI Agent部署后可靠性会随时间下降

Univ of Texas paper shows AI agents can slowly become less reliable after deployment, even when the model itself does not change.

The problem is that agents are often judged when they are fresh, but real agents keep changing because they summarize old chats, store more memories, update facts, and go through maintenance.

An agent that remembers you across weeks is really a small operating system wrapped around a language model: it writes notes, compresses them, retrieves them, updates them, and occasionally cleans house.

Every one of those steps can quietly rot.

A medication dose can become “a daily medication,” two similar clients can blur into one, a canceled subscription can remain active, and a schedule can vanish after a maintenance pass.

The uncomfortable finding is that the agent may still sound competent while becoming less exact.

The proposed AgingBench, a benchmark that checks whether an agent stays reliable across many sessions instead of only checking one clean starting point.

It studies 4 ways agents age: summaries can drop key details, similar memories can get mixed up, updated facts can stay stale, and maintenance can suddenly break memory.

The deeper lesson is that “give it more memory” is often the wrong repair.

If the fact was never written, retrieval cannot save it.

If the fact was written but crowded out, better summarization will not fix it.

If the fact is present but unused, the problem is not storage but the agent’s decision to trust or ignore what it retrieved.

This paper reframes deployed agents less like static models and more like aging infrastructure.

Link – arxiv. org/abs/2605.26302

Title: "Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems"