Developers detail Agent context management from window engineering to long-term memory

On November 5, 2026, at 09:39, Juejin published an article titled "A Detailed Look at Agent Context Management: From Window Engineering to Long-Term Memory". The piece examines how agents manage their context over the course of a run. Its central claim is that a context window functions like RAM rather than a hard drive, and that a larger window does not automatically translate into better performance. The article labels the phenomenon where token volume grows while the model's ability to recall information from the middle of the context degrades as context rot. Against this engineering problem, it explores implementation approaches spanning window engineering and long-term memory. The available excerpt breaks off right after the author states the core judgment, so the article's conclusions about the actual engineering objectives mainstream agents pursue, and the specific techniques it goes on to describe, cannot be confirmed from the material at hand. This is the only report on the topic collected so far, and no other sources have yet corroborated or expanded on its claims. The piece appears to be practitioner-oriented rather than an announcement of a new product, and the material provided does not establish any follow-up developments or community response. Additional reporting would be needed to verify the specific techniques discussed beyond the opening framing.